Showing posts with label coding style. Show all posts
Showing posts with label coding style. Show all posts

Saturday, August 1, 2020

LINT does not do peer reviews

https://pixabay.com/vectors/code-programming-head-computer-2858768/

Once in a while I run into developers who think that peer review can be completely automated by using a good static analysis (generically "lint" or compiler warnings).  In other words, run PC-LINT (or whatever), and when you have no warnings peer review is done.

Nope.

But the reality has some nuance, so here's how I see it.

There are two critical aspects to style:
  (1) coding style for compilers  (will the compiler generate the code you're expecting)
  (2) coding style for humans   (can a human read the code)

A good static analysis tool is good at #1.  Should you run a static analysis tool?  Absolutely.  Pick a good tool.  Or at least do better than -Wall for Gcc (hint, "all" doesn't mean what you think it means (*see note below)).  When your code compiles clean with all relevant warnings turned on, only then is it time for a human peer review.

For #2, capabilities vary widely, and no automated tool can evaluate many aspects of good human-centric coding style.  (Can they use heuristics to help with #1?  Sure.  Can they replace a human?  Not anytime soon.)

My peer review checklist template has a number of items that fall into the #1 bin. The reason is that it is common for embedded software teams to not use static analysis at all, or to use inadequate settings. So the basics are there.  As they become more sophisticated at static analysis, they should delete the automated checks (subsuming them into item #0 -- has static analysis been done?).  Then they should add additional items they've found from experience are relevant to them to re-fill the list to a couple dozen total items.

Summarizing: static analysis tools don't automate peer reviews. They automate a useful piece of them if you are warning-free, but they are no substitute for human judgement about whether your code is understandable and likely to meet its requirements.

Pointers:
* Note: in teaching I require these gcc flags for student projects:
-Werror -Wextra -Wall -Wfloat-equal -Wconversion -Wparentheses -pedantic -Wunused-parameter -Wunused-variable -Wreturn-type -Wunused-function -Wredundant-decls -Wreturn-type -Wunused-value -Wswitch-default -Wuninitialized -Winit-self






Monday, March 12, 2018

Embedded Code Quality and Best Practices Training Videos full length

I've posted the full series of my available embedded system code quality and related best practices videos on YouTube.  These are full-length narrated slides of the core set of safety topics from my new course.  They concentrate on getting the big picture about code quality and good programming practices.
Each of the videos is posted to YouTube as a playlist, with each video covering a slide or two. The full lecture consists of playing the entire play list, with most lectures being 5-7 videos in sequence. (The slide download has been updated for my CMU grad course, so in general has a little more material than the original video. They'll get synchronized eventually, but for now this is what I have.)

Obviously there is more to code quality and safety than just these topics. Additional topics are available slides-only.  You can see the full set of course slides including for those lectures and others here:
  https://users.ece.cmu.edu/~koopman/lectures/index.html#642

Monday, January 29, 2018

New Peer Review Checklist for Embedded C Code

Here's a new peer review checklist to help improve the quality of your embedded C code.

To use the checklist, you should do a sit-down meeting with, ideally, three reviewers not including the code author. Divide the checklist up into three portions as indicated.  Be sure to run decent static analysis before the review to safe reviewer time -- let the tools find the easy stuff before spending human time on the review.

After an initial orientation to what the code is supposed to do and relevant background, the review process is:
  1. The review leader picks the next few lines of code to be reviewed and makes sure everyone is ONLY focused on those few lines.  Usually this is 5-10 lines encompassing a conditional structure, a basic block, or other generally unified small chunk within the code.
  2. Reviewers identify any code problems relevant to their part of the checklist.  It's OK if they notice others, but they should focus on individually considering each item in their part of the checklist and ask "do I see a violation of this item" in just the small chunk of code being considered.
  3. Reviewer comments should be recorded in the form: "Line X seems to violate Checklist Item Y for the following reason." Do NOT suggest a fix -- just record the issue.
  4. When all comments have been recorded, go back to step 1.  Continue to review up to a maximum of 2 hours. You should be covering about 100-200 lines of code per hour. Too fast and too slow are both a problem.
A text version of the checklist is below. You can also download an acrobat version here.  Additional pointers to support materials are after the checklist. If you have a static analysis tool that automates any of the checklist item, feel free to replace that item with something else that's important to you.

===============================================================
Peer Review Checklist: Embedded C Code
       
Before Review:
0    _____    Code compiles clean with extensive warning checks (e.g. MISRA C rules)
       
Reviewer #1:       
1    _____    Commenting:  top of file, start of function, code that needs an explanation
2    _____    Style is consistent and follows style guidelines
3    _____    Proper modularity, module size, use of .h files and #includes
4    _____    No orphans (redundant, dead, commented out, unused code & variables)
5    _____    Conditional expressions evaluate to a boolean value; no assignments
6    _____    Parentheses used to avoid operator precedence confusion
7    _____    All switch statements have a default clause; preferably an error trap
       
Reviewer #2:       
8    _____    Single point of exit from each function
9    _____    Loop entry and exit conditions correct; minimum continue/break complexity
10    _____    Conditionals should be minimally nested (generally only one or two deep)
11    _____    All functions can be unit tested; SCC or SF complexity less than 10 to 15
12    _____    Use const and inline instead of #define; minimize conditional compilation
13    _____    Avoid use of magic numbers (constant values embedded in code)
14    _____    Use strong typing (includes: sized types, structs for coupled data, const)
15    _____    Variables have well chosen names and are initialized at definition
       
Reviewer #3:       
16    _____    Minimum scope for all functions and variables; essentially no globals
17    _____    Concurrency issues? (locking, volatile keyword, minimize blocking time)
18    _____    Input parameter checking is done (style, completeness)
19    _____    Error handling for function returns is appropriate
20    _____    Null pointers, division by zero, null strings, boundary conditions handled
21    _____    Floating point use is OK (equality, NaN, INF, roundoff); use of fixed point
22    _____    Buffer overflow safety (bound checking, avoid unsafe string operations)
       
All Reviewers:     
23    _____    Does the code match the detailed design (correct functionality)?
24    _____    Is the code as simple, obvious, and easy to review as possible?
       
        For TWO Reviewers assign items:   Reviewer#1:  1-11; 23-24    Reviewer#2: 12-24
        Items that are covered with static analysis can be removed from checklist
        Template 1/28/2018:  Copyright CC BY 4.0, 2018, Philip Koopman
===============================================================

Additional material to help you with successful peer reviews:


Monday, November 27, 2017

Embedded Software Course Notes On-Line

I'm just wrapping up my first semester teaching a new course on embedded system software. It covers code quality, safety, and security. Below is table of lecture handouts.

NOTE: there is an update here:
     https://users.ece.cmu.edu/~koopman/lectures/index.html#642
which includes newer course notes and quite a few YouTube videos of these lectures.
You should use that URL instead of this blog post, but I've left this post as-is for Fall 2017.

18-642 Embedded System Software Engineering
Prof. Philip Koopman, Carnegie Mellon University, Fall 2017


SlidesTopics
1Course IntroductionSoftware is eating the world; embedded applications and markets; bad code is a problem; coding is 0% of software; truths and management misconceptions
2Software Development ProcessesWaterfall; swiss cheese model; lessons learned in software; V model; design vs. code; agile methods; agile for embedded
3Global VariablesGlobal vs. static variables; avoiding and removing globals
4Spaghetti CodeMcCabe Cyclomatic Complexity (MCC); SCC; Spaghetti Factor (SF)
5Unit TestingBlack box testing; white box testing; unit testing strategies; MCDC coverage; unit testing frameworks (cunit)
6Modal Code/StatechartsStatechart elements; statechart example; statechart implementation
7Peer ReviewsEffective code quality practices, peer review efficiency and effectiveness; Fagan inspections; rules for peer review; review report; perspective-based reviews; review checklist; case study; economics of peer review
8Code Style/HumansMaking code easy to read; good code hygiene; avoiding premature optimization; coding style
9Code Style/LanguagePitfalls and problems with C; language use guidelines and analysis tools; using language wisely (strong typing); Mars Climate Orbiter; deviations & legacy code
10Testing QualitySmoke testing, exploratory testing; methodical test coverage; types of testing; testing philosophy; coverage; testing resources
11RequirementsAriane 5 flight 501; rules for good requirements; problematic requirements; extra-functional requirements; requirements approaches; ambiguity
12System-Level TestFirst bug story; effective test plans; testing won't find all bugs; F-22 Raptor date line bug; bug farms; risks of bad software
13SW ArchitectureHigh Level Design (HLD); boxes and arrows; sequence diagrams (SD); statechart to SD relationship; 2011 Health Plan chart
14Integration TestingIntegration test approaches; tracing integration tests to SDs; network message testing; using SDs to generate unit tests
15TraceabilityTraceability across the V; examples; best practices
16SQA isn't testingSQA elements; audits; SQA as coaching staff; cost of defect fixes over project cycle
17Lifecycle CMA400M crash; version control; configuration management; long lifecycles
18MaintenanceBug fix cycle; bug prioritization; maintenance as a large cost driver; technical debt
19Process Key MetricsTester to developer ratio; code productivity; peer review effectiveness
33Date Time ManagementKeeping time; time terminology; clock synchronization; time zones; DST; local time; sunrise/sunset; mobility and time; date line; GMT/UTC; leap years; leap seconds; time rollovers; Zune leap year bug; internationalization.
21Floating Point PitfallsFloating point formats; special values; NaN and robots; roundoff errors; Patriot Missile mishap
23Stack OverflowStack overflow mechanics; memory corruption; stack sentinels; static analysis; memory protection; avoid recursion
25Race ConditionsTherac 25; race condition example; disabling interrupts; mutex; blocking time; priority inversion; priority inheritance; Mars Pathfinder
27Data IntegritySources of faults; soft errors; Hamming distance; parity; mirroring; SECDED; checksum; CRC
20Safety+Security OverviewChallenges of embedded code; it only takes one line of bad code; problems with large scale production; your products live or die by their software; considering the worst case; designing for safety; security matters; industrial controls as targets; designing for security; testing isn't enough
Fiat Chrysler jeep hack; Ford Mytouch update; Toyota UA code quality; Heartbleed; Nest thermostats; Honda UA recall; Samsung keyboard bug; hospital infusion pumps; LIFX smart lightbulbs; German steel mill hack; Ukraine power hack; SCADA attack data; Shodan; traffic light control vulnerability; hydroelectric plant vulnerability; zero-day shopping list
22DependabilityDependability; availability; Windows 2000 server crash; reliability; serial and parallel reliability; example reliability calculation; other aspects of dependability
24Critical SystemsSafety critical vs. mission critical; worst case and safety; HVAC malfunction hazard; Safety Integrity Levels (SIL); Bhopal; IEC 61508; fleet exposure
26Safety PlanSafety plan elements; functional safety approaches; hazards & risks; safety goals & safety requirements; FMEA; FTA; safety case (GSN)
28Safety RequirementsIdentifying safety-related requirements; safety envelope; Doer/Checker pattern
29Single Points of FailureFault containment regions (FCR); Toyota UA single point failure; multi-channel pattern; monitor pattern; safety gate pattern; correlated & accumulated faults
30SIL IsolationIsolating different SILs, mixed-SIL interference sources; mitigating cross-SIL interference; isolation and security; CarShark hack
31Redundancy ManagementBellingham WA gasoline pipeline mishap; redundancy for availability; redundancy for fault detection; Ariane 5 Flight 501; fail operational; triplex modular redundancy (TMR) 2-of-3 pattern; dual 2-of-2 pattern; high-SIL Doer/Checker pattern; diagnostic effectiveness and proof tests
32Safety Architecture PatternsSupplemental lecture with more detail on patterns: low SIL; self-diagnosis; partitioning; fail operational; voting; fail silent; dual 2-of-2; Ariane 5 Flight 501; fail silent patterns (low, high, mixed SIL); high availability mixed SIL pattern
34Security PlanSecurity plan elements; Target Attack; security requirements; threats; vulnerabilities; mitigation; validation
35CryptographyConfusion & diffusion; Caesar cipher; frequency analysis; Enigma; Lorenz & Colossus; DES; AES; public key cryptography; secure hashing; digital signatures; certificates; PKI; encrypting vs. signing for firmware update
36Security ThreatsStuxnet; attack motivation; attacker threat levels; DirectTV piracy; operational environment; porous firewalls; Davis Besse incident; BlueSniper rifle; integrity; authentication; secrecy; privacy; LG Smart TV privacy; DoS/DDos; feature activation; St. Jude pacemaker recall
37Security VulnerabilitiesExploit vs. attack; Kettle spambot; weak passwords; master passwords; crypto key length; Mirai botnet attack; crypto mistakes; LIFX revisited; CarShark revisited; chip peels; hidden functionality; counterfeit systems; cloud connected devices; embedded-specific attacks
38Security Mitigation ValidationPassword strength; storing passwords & salt/pepper/key stretching; Adobe password hack; least privilege; Jeep firewall hack; secure update; secure boot; encryption vs. signing revisited; penetration testing; code analysis; other security approaches; rubber hose attack
39Security PitfallsKonami code; security via obscurity; hotel lock USB hack; Kerckhoff's principle; hospital WPA setup hack; DECSS; Lodz tram attack; proper use of cryptography; zero day exploits; security snake oil; realities of in-system firewalls; aircraft infotainment and firewalls; zombie road sign hack

Note that in Spring 2018 these are likely to be updated, so if want to see the latest also check the main course page:  https://www.ece.cmu.edu/~ece642/   For other lectures and copyright notes, please see my general lecture notes & video page: https://users.ece.cmu.edu/~koopman/lectures/index.html


Monday, August 28, 2017

The Spaghetti Factor -- A Software Complexity Metric Proposal


I've had to review code that has spaghetti-level complexity in control flow (too high cyclomatic complexity).  And I've had to review code that has spaghetti-level complexity its data flow (too many global variables mixed together into a single computation).  And I've had to review procedures that just go on for page after page with no end in sight. But the stuff that will really make your brain hurt is code that has all of these problems.

There are many complexity metrics out there. But I haven't seen a one that directly tries to help balance three key points of complexity into a single intuitive number: code complexity, data complexity, and module size. So here is a proposal that could help drive improvement in a lot of the terrible embedded control code I've seen:



The Spaghetti Factor metric (SF):

SF = SCC + (Globals*5) + (SLOC/20)

SCC = Strict Cyclomatic Complexity
Globals = # of read/write global variables
SLOC = # source lines of non-comment code (e.g., C statements)

Scoring:
5-10 - This is the sweet spot for most code except simple helper functions
15 - Don't go above this for most modules
20 - Look closely; review to see if refactoring makes sense
30 - Refactor the design
50 - Untestable; throw the module away and fix the design
75 - Unmaintainable; throw the module away; throw the design away; start over
100 - Nightmare; probably you need to throw the whole subsystem away and re-architect it



Notation:

SCC is Strict Cyclomatic Complexity (sometimes called CC2).  This is a variant of McCabe Cyclomatic complexity (MCC). In general terms, MCC is based on the number of branches in the program. SCC additionally considers complexity based on the number of conditions within each branch. SCC is an approximation of how many test cases it takes to exercise all the paths through code including all the different ways there are to trigger each branch. In other words, it is an estimate of how much work it is to do unit testing. Think of it as an approximation to the effort required for MC/DC testing. But in practice it is also a measure of how hard it is to understand the code.  The idea is to keep SCC low enough that it is feasible to understand and test paths through the code.

Globals is the number of read/write global variables accessed by the module. This does not include "const" values, nor file static variables.  In an ideal world you have zero or near-zero global variables. If you have inherent global state, you should encapsulated that in a state object with appropriate access functions to enforce well-disciplined writes.  Referencing an unstructured pile of dozens or hundreds of global variables can make software difficult to test, and can make subsystem testing almost impossible. Partly that is due to the test scaffolding required, but partly that is simply due to the effort of chasing down all the globals and trying to figure out what they do both inbound and outbound. Moreover, too many globals can make it nearly impossible to chase down bugs or understand the effects of changing one part of the code on the rest of the code. An important goal of this part of the metric is to discourage use of many disjoint global variables to implicitly pass data around from routine to routine instead of passing parameters along with function calls.

SLOC is the number of non-comment "Source Lines of Code."  For C programs, this is the number of programming statements. Typical guidelines are a maximum 100-225 maximum lines of code for a module, with most modules being smaller than that.

As an example calculation, if you have 100 lines of code with an SCC of 9 and 1 global reference, your score will be  SF = 9 + (1*5) + (100/20) = 19.  A score of 19 is on the upper edge of being OK. If you have a distribution of complexity across modules, you'd want most of them to be a bit lower in complexity than this example calculation.

Discussion:

The guideline values are taken primarily from MCC, which typically has a guideline of 10 for most modules, 15 as a usual bound, and 30 as limit.  To account for globals and length, based on my experience, I've changed that to 15 for most modules, 20 as a soft limit and 30 as a hard limit.  You might wish to adjust the threshold and multipliers based on your system and experience. In particular it is easy to make a case that these limits aren't strict enough for life-critical software, and a case can be made for being a little more relaxed in throw-away GUI management code.  But I think this is a good starting point for most every-day embedded software that is written by a human (as opposed to auto-generated code).

The biggest exception is usually what to do about switch statements.  If you exempt them you can end up with multiple switches in one module, or multiple switch/if/switch/if layered nesting.  (Neither is a pretty sight.) I think it is justifiable to exempt modules that have ONLY a switch and conditional logic to do sanity checking on the switch value.  But, because 30 is a pretty generous limit, you're only going to see this rarely. Generally the only legitimate reason to have a switch bigger than that is for something like processing a message type for a communication protocol.  So I think you should not blanket exempt switch statements, but rather include them in an overall case-by-case sign-off by engineering management as to which few exceptions are justifiable.

Some might make the observation that this metric discourages extensive error checking.  That's a different topic, and certainly the intent is NOT to discourage error checking. But the simple answer is that error checking has to be tested and understood, so you can't simply ignore that part of the complexity. One way to handle that situation is to put error checking into a subroutine or wrapper function to get that complexity out of the way, then have that wrapper call the actual function that does the work.  Another way is to break your overall code down into smaller pieces so that each piece is simple enough for you to understand and test both the functionality and the error checking.

Finally, any metric can be gamed, and that is surely true of simple metrics like this one.  A good metric score doesn't necessarily mean your code is fantastic. Additionally, this metric does not consider everything that's important, such as the total number of globals across your code base. On the other hand, if you score poorly on this metric, most likely your code is in need of improvement.

What I recommend is that you use this metric as a way to identify code that is needlessly complex.  It is the rare piece of code indeed that unavoidably needs to have a high score on this complexity metric. And if all your code has a good score, that means it should be that much easier to do peer review and unit testing to ensure that other aspects of the code are in good shape.

References:

A NIST paper on applying metrics is here: http://www.mccabe.com/pdf/mccabe-nist235r.pdf including an interesting discussion of the pitfalls of handling switch statements within a complexity framework.

Monday, July 24, 2017

Don't use macros for MIN and MAX



It is common to see small helper functions implemented as macros, especially in older C code. Everyone seems to do it.  But you should avoid macros, and instead use inline functions.

The motivation for using macros was originally that you needed to use a small function in many places but were worried about the overhead of doing a subroutine call. So instead, you used a macro, which expands into source code in the preprocessor phase.  That was a reasonable tradeoff 40 years ago. Not such a great idea now, because macros cause problems for no good reason.

For example, you might look on the Web and find these common macros
    #define MAX(a,b) ((a) > (b) ? a : b)
    #define MIN(a,b) ((a) < (b) ? a : b)

And you might find that it seems to work for a while.  You might get bitten by the missing "()" guards around the second copy of a and b in the above -- which version you get depends on which cut & paste code site you visit. 

But then you'll find that there are still weird situations where you get unexpected behavior. For example, what does this do?
    c = MAX(a++, b);
If a is greater than b executing the code will increment a twice, but if a is less than or equal to b it will only increment a once.  And if you start mixing types or putting complicated expressions into the macro things can get weird and buggy in a hurry.

Another related problem is that the macro will expand, increasing the cyclomatic complexity of your code. That's because a macro is equivalent to you having put the conditional branch into the source code. (Remember, macro expansion is done by the preprocessor, the so compiler itself acts as if you'd typed the conditional assignment expression every place you use the macro.) This complexity rating is justified, because there is no actual procedure that can be unit tested independently.

As it turns out, macros are evil. See the C++ FAQ: https://isocpp.org/wiki/faq/misc-technical-issues#macros-with-if  which lists 4 different types of evil behavior.  There are fancy hacks to try to get any particular macros such as MIN and MAX to be better behaved, but no matter how hard you try you're really just making a deal with the devil. 

What's the fix?

The fix is: don't use macros. Instead use inline procedure calls.

You should already have access to built-in functions for floating point such as fmin() and fmax().  If it's there, use the stuff from your compiler vendor instead of writing it yourself!

If your compiler doesn't have integer min and max, or you are worried about breaking existing macro code, convert the macros into inline functions with minimal changes to your code base:

inline int32_t MAX(int32_t a, int32_t b) { return((a) > (b) ? a : b); }
inline int32_t MIN(int32_t a, int32_t b) { return((a) < (b) ? a : b); }

If you have other types to deal with you might need different variants depending on the types, but often a piece of code uses predominantly one data type for its calculations, so in practice this is usually not a big deal. And don't forget, if your build environment has a built in min or max you can just set up the macro to call that directly.

What about performance?

The motivation for using macros back in the bad old days was efficiency. A subroutine call involved a lot of overhead. But the inline keyword tells the compiler to expand the code in-place while retaining all the advantages of a subroutine call.  Compilers are pretty good at optimization these days. So there is no overhead at run-time.  I've also seen advice to put the inline function in a header file so it will be visible to any procedure needing it, and the macro was already there anyway.

Strictly speaking, "inline" is a suggestion to the compiler. However, if you have a decent compiler it will follow the suggestion unless the inline function is so big the call overhead just doesn't matter. Some compilers have a warning flag that will let you know when the inline didn't happen.  For example, use -Winline for gcc.  If your compiler ignores "inline" for something as straightforward as MIN or MAX, get a different compiler.

What about multiple types?

A perceived advantage of the macro approach is that you can play fast and loose with types.  But playing fast and loose with types is a BAD IDEA because you'll get bugs.  

If you really hate having to match the function name to the data types then what you need is to switch to a language that can handle this by automatically picking the right function based on the operator types. In other words, switch from a to a language that is 45 years old (C) to one that is only about 35 years old (C++).  There's a paper from 1995 that explains this in the context of min and max implemented with templates:  http://www.aristeia.com/Papers/C++ReportColumns/jan95.pdf
As it turns out the rabbit hole goes a lot deeper than you might think for a generic solution.

But you don't have to go down the rabbit hole.  For most code the best answer is simply to use inline functions and pick the function name that matches your data types. You shouldn't lose any performance at all, and you'll be likely to save a lot of time chasing obscure bugs.

Monday, May 22, 2017

#define vs. const

Is your code full of "#define" statements?  If so, you should consider switching to the const keyword.

Old school C:
    #define MYVAL 7

Better approach:
   const uint32_t myVal = 7;

Here are some reasons you should use const instead of #define:
  • #define has global scope, so you're creating (read-only) global values every time you use #define. Global scope is evil, so don't do that.  (Read-only global scope for constant values is a bit less evil than global variables per se, especially if you can't use the namespace features of C++. But gratuitous global scope is always a bad idea.) A const alternative can obey scoping rules, including being purely local if defined inside a procedure, or more commonly file static with the "static" keyword.
  • Const lets you do more aggressive type checking (depending upon your compiler and static analysis tools, especially if you use a typedef more specific than built-in C data types). While C is a bit weak as a language in this area compared to other languages, a classical example is a const lets you identify a number as being in feet or meters, while the #define approach is just as if you'd typed the number 7 in with no units. The #define approach can bite you if you use the wrong value in the wrong place. Type checking is an effective way to find bugs, and using #define gives up an opportunity to let static analysis tools help you with that.
  • Const lets you use the value as if it were a variable when you need to (e.g., passing an address to the variable) without having to change how the variable is defined.
  • #define in general is so bug-prone that you should minimize its use just to avoid having to spend time asking "is this one OK?" in a peer review. Most #define uses tend to be const variables in old-school code, so getting rid of them can dramatically reduce the peer review burden of sifting through hundreds of #define statements to look for problems.
Here are some common myths about this tradeoff. (Note that on some systems these statements might be true, especially if you have and old and lame compiler.  But they don't necessarily have to be true and they often are false, especially on newer chips with newer compilers.)
  • "Const wastes memory."  False if you have a compiler that is smart enough to do the right thing. Sure, if you want to pass a pointer to the const it will actually have to live in memory somewhere, but you can't even pass a pointer to a #define at all. One of the points of "const" is to give the compiler a hint that lets it optimize memory footprint.
  • "Const won't work for X." Generally false if you have a newer compiler, and especially if you are using a mostly-C subset of the capability of a C++ compiler, as is increasingly common. And honestly, most of the time #define is just being used as a plain old integer const to get rid of magic numbers. const will work fine.  (If you have magic numbers instead of #define, then you have bigger problems than this even.) Use const for the no-brainer cases. Something is probably wrong if everything about your code is so special you need #define everywhere.
  • "Const hassles me about type conversions."  That's a feature to prevent you from being sloppy!  So strictly speaking the compiler doing this is not a myth. The myth is that this is a bad thing.
There are plenty of discussions on this topic.  You'll also see that some folks advocate using enums for some situations, which we'll get to another time. For now, if you change as many #defines as you can to consts then that is likely to improve your code quality, and perhaps flush out a few bugs you didn't realize you had.

Be careful when reading discussion group postings on this topic.  There is a lot of dis-information out there about performance and other potential tradeoff factors, usually based on statements about 20 year old versions of the C language or experiences with compilers that have poor optimization capability.  In general, you should always use const by default unless your particular compiler/system/usage presents a compelling case not to.

See also the Barr Group C coding standard rule 1.8.b which says to use const, and has a number of other very useful rules.


Monday, May 8, 2017

Optimize for V&V, not for writing code



Writing code should be made more difficult so that Verification &Validation can be made easier.

I first heard this notion years ago at a workshop in which several folks from industry who build high assurance software (think flight controls) stood up and said that V&V is what matters. You might expect that from flight control folks, but their reasoning applies to pretty much every embedded project. That's because it is a matter of economics. 

Multiple speakers at that workshop said that aviation software can require 4 or 5 hours of V&V for every 1 hour of creating software. It makes no economic sense to make life easy for the 1 hour side of the ratio at the expense of making life painful for the 5 hour side of the ratio.

Good, but non-life-critical, embedded software requires about 2 hours of V&V for every 1 hour of code creation. So the economic argument still holds, with a still-compelling multiplier of 2:1.  I don't care if you're Vee,  Agile, hybrid model or whatever. You're spending time on V&V, including at least some activities such as peer review, unit test, created automated tests, performing testing, chasing down bugs, and so on. For embedded products that aren't flaky, probably you spend more time on V&V than you do on creating the code. If you're doing TDD you're taking an approach that has the idea of starting with a testing viewpoint built in already, by starting from testing and working outward from there. But that's not the only way to benefit from this observation.

The good news is that making code writing "difficult" does not involve gratuitous pain. Rather, it involves being smart and a bit disciplined so that the code you produce is easier for others to perform V&V on. A bit of up front thought and organization can save a lot on downstream effort. Some examples include:
  • Writing concise but helpful code comments so that reviewers can understand what you meant.
  • Writing code to be obvious rather than clever, again to help reviewers.
  • Follow a style guide to make your code consistent, and thus easier to understand.
  • Writing code that compiles clean for static analysis, avoiding time wasted finding defects in test that a tool could have found, and avoiding a person having to puzzle out which warnings matter, and which don't.
  • Spending some time to make your unit interfaces easier to test, even if it requires a bit more work designing and coding the unit.
  • Spending time making it easy to trace between your design and the code. For example, if you have a statechart, make sure the statechart uses names that map directly to enum names rather than using arbitrary state variables such as "magic number" integers between 1 and 7. This makes it easier to ensure that the code and design match. (For that matter, just using statecharts to provide a guide to what the code does also helps.)
  • Spending time up front documenting module interaction so that integration testers don't have to puzzle out how things are supposed to work together. Sequence diagrams can help a lot.
  • Making the requirements both testable and easy to trace. Make every requirement idea a stand-alone sentence or paragraph and give it a number so it's easy to trace to a specific test primarily designed to test that particular requirement. Avoid having requirements in huge paragraphs of free-form text that mix lots of different concepts together.
Sure, these sound like a good idea, but many developers skip or skimp on them because they don't think they can afford the time. They don't have time to make their code clean because they're too busy writing bugs to meet a deadline. Then they, and everyone else, pay for this during the test cycle. (I'm not saying the programmers are necessarily the main culprits here, especially if they didn't get a vote on their deadline. But that doesn't change the outcome.)

I'm here to say you can't afford not to follow these basic code quality practices. That's because every hour you're saving by cutting corners up front is probably costing you double (or more) downstream by making V&V more painful than it should be. It's always hard to invest in downstream benefits when the pressure is on, but doing so is costing you dearly when you skimp on code quality.

Do you have any tricks to make code easier to understand that I missed?

Monday, January 9, 2017

Language Use (Coding Style for Compilers) Overview Video

Here's a summary video on Language Use (Coding Style for Compilers) which is half of the topic of coding style.

Other pointers on this topic (my blog posts unless otherwise noted):
For more about Edge Case Research and how to subscribe to our video training channel, please see this Blog posting.

Monday, December 5, 2016

Understandable Code (Coding Style for Humans) (Preview)

Here's a summary video on Human-Understandable Code (Code Readability) which is half of the topic of coding style.



Other pointers on this topic (my blog posts unless otherwise noted):

For more about Edge Case Research and how to subscribe to our video training channel, please see this Blog posting.

Monday, November 14, 2016

Spaghetti Code and Complexity Tutorial

Here's a preview video on Spaghetti Code and Cyclomatic Complexity.  There is also a full version of this video available for free from the Edge Case Research video library (see below for details).

Notes:

Spaghetti Code Preview [ECR] .


Full tutorial video: https://vimeo.com/185732981

For more about Edge Case Research and how to subscribe to our video training channel, please see this Blog posting.


Monday, March 7, 2016

Multiple Returns and Error Checking

Summary: Whether or not to allow multiple returns from a function is a controversial matter, but I recommend having a single return statement AND avoiding use of goto.

Discussion:

If you've been programming robust embedded systems for a while, you've seen code that looks something like this:

int MyRoutine(...)
{ ...
  if(..something fails..) { ret = 0; }
  else 
  { .. do something ...
    if(..somethingelse fails..) {ret = 0;}
    else 
    { .. do something ..
      if(...yetanotherfail..) {ret = 0;}
      else 
      { .. do the computation ...
        ret = value; 
      }
    }
  }
  // perform default function
  return(ret);
}

(Note: the "ret = 0" and "ret = value" parts are usually more complex; I'm keeping this simple for the sake of the discussion.)

The general pattern to this code is doing a bunch of validity checks and then, finally, in the most deeply nested "else" doing the desired action. Generally the code in the most deeply nested "else" is actually the point of the entire routine -- that's the action that you really want to take if all the exception checks pass.  This type of code shows up a lot in a robust embedded system, but can be difficult to understand.

This problem has been dealt with a number of times and most likely every reasonable idea has been re-invented many times. But when I ran up against it in a recent discussion I did some digging and found out I mostly disagree with a lot of the common wisdom, so here is what I think.

A common way to simplify things is the following, which is the guard clause pattern described by Martin Fowler in his refactoring catalog (an interesting resource with accompanying book if you haven't run into it before.)

int MyRoutine(...)
{ ...
  if(..something fails..)     {return(0);}
  if(..somethingelse fails..) {return(0);}
  if(...yetanotherfail..)     {return(0);}
  // perform default function
  return(value);
}

Reasonable people can (and do!) disagree about how to handle this.   Steve McConnell devotes a few pages of his book Code Complete (Section 17.1, and 17.3 in the second edition) that covers this territory, with several alternate suggestions, including the guard clause pattern and a discussion that says maybe a goto is OK sometimes if used carefully.

I have a somewhat different take, possibly because I specialize in high-dependability systems where being absolutely sure you are not getting things wrong can be more important than subjective code elegance. (The brief point of view is: getting code just a little klunky but obviously right and easy to check for correctness is likely to be safe. Getting code to look nice, but with a residual small chance of missing a subtle bug might kill someone. More this line of thought can be found in a different posting.)

Here is a sketch of some of the approaches I've seen and my thoughts on them:

(1) Another Language. Use another language that does a better job at this.  Sure, but I'm going to assume that you're stuck with just C.

(2) Guard Clauses.  Use the guard class pattern with early returns as shown above . The downside of this is that it breaks the single return per function rule that is often imposed on safety critical code. While a very regular structure might work out OK in many cases, things get tricky if the returns are part of code that is more complex. Rather than deal with shades of gray about whether it is OK to have more than one return, I prefer a strict single-return-per-function rule. The reason is that it avoids having to waste a lot of time hashing out when multiple returns are OK and when they aren't -- and the accompanying risk of getting that judgment call wrong. In other words black-and-white rules take less interpretation.

If you are building a safety critical system, every subjective judgement call as to what is OK and what is not OK is a chance to get it wrong.  (Could you get airtight design rules in place? Perhaps. But you still need to check them accurately after every code modification. If you can't automate the checks, realistically the code quality will likely degrade over time, so it's a path I prefer not to go down.)

(3) Else If Chain.  Use an if/else chain and put the return at the end:

int MyRoutine(...)
{ ...
  if(..something fails..)          {ret=0;}
  else if(..somethingelse fails..) {ret=0;}
  else if(...yetanotherfail..)     {ret=0;}
  else { // perform default function
         ret = value;
        }
  return(ret);
}

Wait, isn't this the same code with flat indenting?  Not quite.  It uses "else if" rather than "else { if". That means that the if statements aren't really nested -- there is really only a single main path through the code (a bunch of checks and the main action in the final code segment), without the possibility of lots of complex conditions in the "if" sidetrack branches.

If your code is flat enough that this works then this is a reasonable way to go. Think of it as guard clauses without the multiple returns.  The regular "else if" structure makes it pretty clear that this is a sequence of alternatives, or often a set of "check that everything is OK and in the end take the action." However, there may be cases in which the code logic is difficult to flatten this way, and in those cases you need something else (keep reading for more ideas).  The trivial case is:

(3a) Simplify To An Else If Chain.  Require that all code be simplified enough that an "else if" chain works.  This is a nice goal, and my first choice of style approaches.  But sometimes you might need a more capable and flexible approach.  Which brings us to the rest of the techniques...

(4) GOTO. Use a "goto" to break out of the flow and jump to the end of the routine.  I have seen many discussion postings saying this is the right thing to do because the code is cleaner than lots of messy if/else structures. At the risk of being flamed for this, I respectfully disagree. The usual argument is that a good programmer exhibiting discipline will do just fine.  But that entirely misses what I consider to be the bigger picture.  I don't care how smart the programmer who wrote the code is (or thinks he is).  I care a lot about whoever has to check and maintain the code. Sure, a good programmer on a good day can use a "goto" and basically emulate a try/throw/catch structure.  But not all programmers are top 10 percentile, and a lot of code is written by newbies who simply don't have enough experience to have acquired mature judgment on such matters. Beyond that, nobody has all good days.

The big issue isn't whether a programmer is likely to get it right. The issue is how hard (and error-prone) it is for a code reviewer and static analysis tools to make sure the programmer got it right (not almost right, or subtly wrong). Using a goto is like pointing a loaded gun at your foot. If you are careful it won't go off. But even a single goto shoves you onto a heavily greased slippery slope. Better not to go there in the first place. Better to find a technique that might seem a little more klunky, but that gets the job done with minimum fuss, low overhead, and minimal chance to make a mistake. Again, see my posting on not getting things wrong.

(Note: this is with respect to unrestricted "goto" commands used by human programmers in C. Generated code might be a different matter, as might be a language where "goto" is restricted.)

(5) Longjmp.  Use setjmp/longjmp to set a target and then jump to that target if the list of "if" error checks wants to return early. In the final analysis this is really the moral equivalent of a "goto," although it is a bit better controlled. Moreover, it uses a pointer to code (the setjmp/longjmp variable), so it is an indirect goto, and that in general can be hazardous. I've used setjmp/longjmp and it can be made to work (or at least seem to work), but dealing with pointers to code in your source code is always a dicey proposition. Jumping to a corrupted or uninitialized pointer can easily crash your system (or sometimes worse).  I'd say avoid using this approach.

I've seen discussion forum posts that wrap longjmp-based approaches up in macros to approximate Try/Throw/Catch. I can certainly appreciate the appeal of this approach, and I could see it being made to work. But I worry about things such as whether it will work if the macros get nested, whether reviewers will be aware of any implementation assumptions, and what will happen with static analysis tools on those structures. In high-dependability code if you can't be sure it will work, you shouldn't do it.

(6) Do..While..Break. Out of the classical C approaches I've seen, the one I like the most (beyond else..if chains) is using a "do..while..break" structure:

int MyRoutine(...)
{ ...
  do { //  start error handling code sequence
    if(..something fails..)     {ret=0; break;}
    if(..somethingelse fails..) {ret=0; break;}
    if(...yetanotherfail..)     {ret=0; break;}
    // perform default function
    ret = value;
  } while (0); // end error handling code sequence
  return(ret);
}

This code is a hybrid of the guard pattern and the "else if" block pattern. It uses a "break" to skip the rest of the code in the code block (jumping to the while(0), which always exits the loop). The while(0) converts this structure from a loop into just a structured block of code with the ability to branch to the end of the code block using the "break" keyword. This code ought to compile to an executable that is has essentially identical efficiency to code using goto or code using an else..if chain. But, it puts an important restriction on the goto-like capability -- all the jumps have to point to the end of the do..while without exception.

What this means in practice is that when reviewing the code (or a change to the code) there is no question as to whether a goto is well behaved or not. There are no gotos, so you can't make an unstructured goto mistake with this approach.

While this toy example looks pretty much the same as the "else if" structure, an important point is that the "break" can be placed anywhere -- even deeply within a nested if statement -- without raising questions as to what happens when the "break" is hit. If in doubt or there is some reason why this technique won't work for you, I'd suggest falling back on restructuring the code so "else if" or this technique works if the code gets too complex to handle. The main problem to keep in mind is that if you nest do..while structures the break will un-nest only one level.

I recognize that this area falls a little bit into a matter of taste and context. My taste is for code that is easy to review and unlikely to have bugs. If that is at odds with subjective notions of elegance, so be it. In part my preference is to outlaw the routine use of techniques that require manual analysis to determine of a potentially unsafe structure is being used the "right" way. Every such judgement call is a chance to get it wrong, and a distraction of human reviewer attention away from more important things. And I dislike arguments of the form that a "good" and experienced programmer won't make a mistake. It is just too easy to miss a subtle bug, especially when you're modifying code in a hurry.

If you've run into another way to handle this problem let me know.

Update 11/2022:  Olayiwola Ayinde has a similar take, pointing out that multiple exits make it too easy to forget to de-allocate resources that might have been allocated at the start of a procedure:

Monday, November 17, 2014

Not Getting Software Wrong


The majority of time creating software is typically spent making sure that you got the software right (or if it's not, it should be!).  But, sometimes, this focus on making software right gets in the way of actually ensuring the software works.  Instead, in many cases it's more important to make sure that software is not wrong.  This may seem like just a bit of word play, but I've come to believe that it reflects a fundamental difference in how one views software quality.

Consider the following code concept for stopping four wheel motors on a robot:

// Turn off all wheel motors
  for (uint_t motor = 1; motor < maxWheels; motor++)  {
    SetSpeed(motor, OFF);
  }

Now consider this code:
// Turn off all wheel motors
  SetSpeed(leftFront,  OFF);
  SetSpeed(rightFront, OFF);
  SetSpeed(leftRear,   OFF);
  SetSpeed(rightRear,  OFF);

(Feel free to make obvious assumptions, such as leftFront being an const uint_t, and modify to your favorite naming conventions and style guidelines. None of that is the point here.)

Which code is better?   Well, from our intro to programming course probably we want to write the first type of code. It is more flexible, "elegant," and uses the oh-so-cool concept of iteration.  On the other hand, the second type of code is likely to get our fingers smacked with a ruler in a freshman programming class.  Where's the iteration?  Where is the flexibility? What if there are more than four items that need to be turned off? Where is the dazzling display of (not-so-advanced) computer science concepts?

But hold on a moment. It's a four wheeled robot we're building.  Are you really going to change to a six wheeled robot?  (Well, maybe, and I've even worked a bit with such robots.  But sticking on two extra wheels isn't all that common after the frame has been welded!)  So what are you really buying by optimizing for a change that is unlikely to happen?

Let's look at it a different way. Many times I'm not interested in elegance, clever use of computer science concepts, or even number of lines of code. What I am interested in is that the code is actually correct. Have you ever written the loop type of code and gotten it wrong?  Be honest!  There is a reason that "off by one error" has its own wikipedia entry. How long did it take you to look at the loop and make sure it was right? You had to assume or go look up the value of maxWheels, right?  If you are writing unit tests, you need to somehow figure out a way to test that all the motors got turned off -- and account for the fact that maxWheels might not even be set to the correct value.

But, with the second set of code, it's pretty easy to see that all four wheels are getting turned off.  There's no loop to get wrong.  (You do have to make sure that the four wheel consts are set properly.)  There is no loop to test.  Cyclomatic complexity is lower because there is no looping branch.  You don't have to mentally execute the loop to make sure there is no off-by-one error. You don't have to ask whether the loop can (or should) execute zero times. Instead, with the second set of codes you can just look at it and count up that the wheels are being turned off.

Now of course this is a pretty trivial example, and no doubt one that at least someone will take exception to.  But the point I'm making is the following.  The first code segment is what we were all taught to do, and arguably easier to write. But the second code segment is arguably easier to check for correctness.  In peer reviews someone might miss a bug in the first code. I'd say that missing a bug is less likely in the second code segment.

Am I saying that you should avoid loops if you have 10,000 things to initialize?  No, of course not. Am I saying you should cut and paste huge chunks of code to avoid a loop?  Nope. And maybe you actually do want to use the loop approach in your situation even with 3 or 4 things to initialize for a good reason. And if you get lots of lines of code that is likely to be more error-prone than a well-considered loop. All that I'm saying is that when you write code, consider carefully the tradeoff between writing concise, but clever, code and writing code that is hard to get wrong. Importantly, this includes the risk of a varying style causing an increased risk of getting things wrong. How that comes out will depend on your situation.  What I am saying is think about what is likely to change, what is unlikely to change, and what the path is to least risk of having bugs.

By the way, did you see the bug?  If you did, good for you.  If not, well, then I've already proved my point, haven't I?  The wheel number should start at 0 or the limit should be maxWheels+1.  Or the "<" should be "<=" -- depending on whether wheel numbers are base 0 or base 1.  As it is, assuming maxWheels is 4 as you'd expect, only 3 out of 4 wheels actually get turned off.  By the way, this wasn't an intentional trick on the reader. As I was writing this article, after a pretty long day, that's how I wrote the code. (It doesn't help that I've spent a lot of time writing code in languages with base-1 arrays instead of base-0 arrays.) I didn't even realize I'd made that mistake until I went back to check it when writing this paragraph.  Yes, really.  And don't tell me you've never made that mistake!  We all have. Or you've never tried to write code at the end of a long day.  And thus are bugs born.  Most are caught, as this one would have been, but inevitably some aren't.  Isn't it better to write code without bugs to begin with instead of find (most) bugs after you write the code?

Instead of trying to write code and spending all our effort to prove that whatever code we have written is correct, I would argue we should spend some of our effort on writing code that can't be wrong.  (More precisely, code that is difficult to get wrong in the first place, and easy to detect is wrong.)  There are many tools at our disposal to do this beyond the simple example shown.  Writing code in a style that makes very rigorous static analysis tools happy is one way. Many other style practices are designed to help with this as well, chief among them being various type checking strategies, whether automated or implemented via naming conventions. Avoiding high cyclomatic complexity is another way that comes to mind. So is creating a statechart before actually writing the code, then using a switch statement that traces directly to the statechart. Avoiding premature optimization is also important in avoiding bugs.  The list goes on.

So the next time you are looking at a piece of code, don't ask yourself "do I think this is right?"  Instead, ask yourself "how easy is it to be sure that it is not wrong?"  If you have to think hard about it -- then maybe it really is incorrect code even if you can't see a bug.  Ask whether it could be rewritten in a way that is more obviously not wrong.  Then do it.

Certainly I'm not the first to have this idea. But I see small examples of this kind of thing so often that it's worth telling this story once in a while to make sure others have paused to consider it.  Which brings us to a quote that I've come to appreciate more and more over time.  Print it out and stick it on the water cooler at work:

"There are two ways of constructing a software design: One way is to make it so simple that there are obviously no deficiencies and the other way is to make it so complicated that there are no obvious deficiencies."

        — C.A.R. Hoare, The 1980 ACM Turing Award Lecture  (CACM 24(2), Feb 1981, p. 81)

(I will admit that this quote is a bit clever and therefore not a sterling example of making a statement easy to check for correctness.  But then again he is the one who got the Turing Award, so we'll allow some slack for clever wording in his acceptance essay.)

Monday, August 25, 2014

Use of Static Analysis Tools

Critical embedded software should use static checking tools with a defined and appropriate set of rules, and should have zero warnings from those tools.

Consequences:
While rigorous peer reviews can catch many defects, some misuses of language are easy for humans to miss but straightforward for a static checking tool to find. Failing to use a static checking tool exposes software to a needless risk of defects. Ignoring or accepting the presence of large numbers of warnings similarly exposes software to needless risk of defects.

Accepted Practices:
  • Using a static checking tool that has been configured to automatically check as many coding guideline violations as practicable. For automotive applications, following all or almost all (with defined and justified exceptions) of the MISRA C coding standard rules is an accepted practice.
  • Ensuring that code checks “clean,” meaning that there are no static checking violations.
  • In rare instances in which a coding rule violation has been formally approved, use pragmas to formally document the deviation and direct the static checking tool not to issue a warning.
Discussion:
Static checking tools look for suspicious coding structures and data use within a piece of software. Traditionally, they look for things that are “warnings” instead of errors. The distinction is that an error prevents the compiler from being able to generate code that will run. In contrast, a warning is an instance in which code can be compiled, but in which there is a substantial likelihood that the code the compiler generates will not actually do what the designer wants it to do. Reasons for a warning might include ambiguities in the language standard (the code does something, but it’s unclear whether what it does is what the language standard meant), gaps in the language standard (the code does something arbitrary because the language standard does not standardize behavior for this case), and dangerous coding practices (the code does something that is probably a bad idea to attempt). In other words, warnings point out potential code defects. Static analysis capabilities vary depending upon the tool, but in general are all designed to help find instances of poor use of a programming language and violations of coding rules.

An analogous example to a static checking tool is the Microsoft Word grammar assistant. It tells you when it thinks a phrase is incorrect or awkward, even if all the words are spelled correctly. This is a loose analogy because creativity in expression is important for some writing. But safety critical computer code (and English-language writing describing the details of how such systems work) is better off being methodical, regular, and precise, rather than creatively expressed but ambiguous.

Static checking tools are an important way of checking for coding style violations. They are particularly effective at finding language use that is ambiguous or dangerous. While not every instance of a static checking tool warning means that there is an actual software defect, each warning given means that there is the potential for a defect. Accepted practice for high quality software (especially safety critical software) is to eliminate all warnings so that the code checks “clean.” The reasons for this include the following. A warning may seem to be OK when examined, but might become a bug in the context of other changes made to the code later. A multitude of warnings that have been investigated and deemed acceptable may obscure the appearance of a new warning that indicates an actual bug. The reviewer may not understand some subtle language-dependent aspect of a warning, and thus think things are OK when they are actually not.

Selected Sources:
MISRA Guidelines require the use of “automatic static analysis” for SIL 3 automotive systems and above, which tend to be systems that can kill or severely injure at least one person if they fail (MISRA Guidelines, pg. 29). The guidelines also give this guidance: “3.5.2.6 Static analysis is effective in demonstrating that a program is well structured with respect to its control, data, and information flow. It can also assist in assessing its functional consistency with its specification.”

McConnell says: “Heed your compiler's warnings. Many modern compilers tell you when you have different numeric types in the same expression. Pay attention! Every programmer has been asked at one time or another to help someone track down a pesky error, only to find that the compiler had warned about the error all along. Top programmers fix their code to eliminate all compiler warnings. It's easier to let the compiler do the work than to do it yourself.” (McConnell, pg. 237, emphasis added).

References:
  • McConnell, Code Complete, Microsoft Press, 1993.
  • MISRA, (MISRA C), Guideline for the use of the C Language in Vehicle Based Software, April 1998.
  • MISRA, Development Guidelines for Vehicle Based Software, November 1994 (PDF version 1.1, January 2001).
  • (See also posting on Coding Style Guidelines and MISRA C)



Monday, August 11, 2014

Coding Style Guidelines and MISRA C

Critical embedded software should follow a well-defined set of coding guidelines, enforced with comprehensive static checking tools, with essentially no deviations. MISRA C is an example of an accepted set of such coding guidelines.

Consequences:
Coding style guidelines exist to make it more difficult to make mistakes, and also to make it easier to detect when mistakes have been made. Failing to establish or follow formal, written coding guidelines makes it more difficult to understand code, leading to less effective code reviews and a reasonable expectation of increased levels of software defects. Failing to follow the language usage rules defined by a coding style guideline leads to using the language in a way that can normally be expected to result in poorly defined or incorrect software behaviors, increases the risk of software defects.

Accepted Practices:

  • All projects should follow a written coding style guideline document.
  • Coding guidelines should address formatting, commenting, name use, and other similar topics.
  • Coding guidelines should address good language usage practices to create understandable code and reduce the chance of introducing software defects.
  • Coding guidelines should specifically address which language features and usage patterns should be avoided as being error-prone, dangerous, or undefined by the language standard.
  • Coding guidelines should be followed with essentially no exception. Exceptions should require formal review with written approval and annotations in the source code. If guidelines are inappropriate, the guidelines should be changed.

Discussion:
Style in software can be considered analogous to style in writing. Compilers enforce some basic programming language construction rules that allow the source code to be compiled into executable software. Style, on the other hand, has more to do with how ideas are expressed within the constraints of the programming language used. Some style considerations have to do with variable naming conventions, indentation, and physical organization aspects of lines of code. Other style considerations have to do with the use of the programming language itself. Some constructs in a programming language are ambiguous or easily misunderstood. And some constructions in software, while correct in terms of language definition, are very likely to indicate a software defect.

A classic example of a subtle defect in the C programming language is:
“if (x = y) { …}”   The programmer almost always means to compare “x” and “y” for equality, but the C programming language is defined such that this code instead copies the value of “y” into “x” and then tests to see if the result of that copy operation was non-zero. The correct code would be “if (x == y) { … }” which adds a second “=” to make the operation a comparison instead of an assignment operation. Using “=” instead of “==” in conditionals is a common mistake when creating C programs, easy to confuse visually, and is therefore prohibited by typical style guidelines even though the single “=” version of the code is a valid language construct. A loose analogy might be a prohibition against using multiple negatives in an English language sentence because it is too difficult to not not not not not not (sic) make a mistake with such a sentence even though the meaning is unambiguous if the sentence is carefully (and correctly) analyzed.

It is accepted practice to have a defined set of coding guidelines that cover all relevant aspects of programming language use. Such guidelines are typically tailored for each project, but once defined should be followed rigorously. Guidelines typically cover code formatting, commenting, use of names, language use conventions, and other relevant aspects. While guidelines can be tailored per project, there are nonetheless a number of generally accepted practices for reducing the chance of software defects (such as forbidding a single “=” in a conditional evaluation as just discussed).

Of particular concern in safety critical software are language use rules to avoid ambiguity and hazardous language constructs. It is a generally accepted practice to outright ban hazardous and error-prone language structures to avoid the chance of defects, even if doing so makes software a bit less convenient to write and those structures would otherwise be a legal use of the language. In other words, an essential aspect of coding style for safety critical systems is outlawing code structures that are technically valid but are too dangerous or error-prone to use. The result is a written document that defines coding style in general, and language usage rules in particular. These rules must be applied rigorously and with essentially no exceptions. (The “no exceptions” part is feasible because it is acceptable to tailor the rules to the particular project. So it is not a matter of applying arbitrary rules and making exceptions, but rather picking rules that make sense for the situation and then rigorously sticking to them.) In short, every safety critical software project must have a coding style guide and must follow it rigorously to achieve acceptable levels of safety.

It is often the case that it makes sense to adopt an existing set of language use rules rather than make up your own. The MISRA C set of coding rules was specifically created for safety critical automotive software, and is the most well known C programming language subset for safety critical software. A typical practice when writing safety critical C code is to start with MISRA C, create a defined set of which rules will be followed (usually this is almost all of them), and then follow those rules rigorously. Exceptions to adopted rules should be very few, granted only after a formal written review process, and documented in the code as to the type of exception and reason for granting it. Preferably, automated tools (widely available for MISRA C as discussed in Jones 2002, pg. 56) are used to enforce the rules in addition to a required peer review of code.

It is accepted practice to adopt new coding style rules when better practices come into use, and apply those coding style rules to existing code when that code is being updated and incorporated into a new product.

Selected Sources:
The MISRA C guidelines (MISRA C 1998) are specifically designed for safety critical systems at SIL 2 and above. (MISRA Report 2 p. ix) They consist of a list of rules about coding practices to use and practices to avoid. They concentrate primarily on language use rather than code formatting. While MISRA C was originally developed for automotive applications, it was being set forth as a more general standard for adoption in other domains by 2002 (e.g., Jones 2002).  Over time, MISRA C has transition beyond just automotive applications to mainstream use for high integrity software in other areas. A predecessor of MISRA C is the list of rules in the book Safer C (Hatton, 1995).

More general coding style guidelines abound. It is easy to find a coding style guideline that can be adapted for the specifics of a project. Examples include chapter 18 of McConnell (1993).

NASA says that it is important that all levels of the project agree to the coding standards, and that they are enforced.   (NASA 2004 pg. 146)

Enforcing coding style involves the use of static checking in addition to formal peer reviews. Beyond the general consensus in the software community that following a defined coding style is a good idea, Nagappan and Ball found that “there exists a strong positive correlation between the static analysis defect density and the pre-release defect density determined by testing. Further, the predicted pre-release defect density and the actual pre-release defect density are strongly correlated at a high degree of statistical significance.” (Nagappan 2005, abstract)  In other words, modules that fail to follow a coding style as determined by static analysis have more bugs.

McConnell says: “Heed your compiler's warnings. Many modern compilers tell you when you have different numeric types in the same expression. Pay attention! Every programmer has been asked at one time or another to help someone track down a pesky error, only to find that the compiler had warned about the error all along. Top programmers fix their code to eliminate all compiler warnings. It's easier to let the compiler do the work than to do it yourself.” (McConnell 1993, pg. 237).

MISRA Software Guidelines require the use of “automatic static analysis” for SIL 3 automotive systems and above, which tend to be systems that can kill or severely injure at least one person if they fail (MISRA Guidelines 1994, pg. 29). The guidelines also give this guidance: “3.5.2.6 Static analysis is effective in demonstrating that a program is well structured with respect to its control, data, and information flow. It can also assist in assessing its functional consistency with its specification.” IEC 61508 highly recommends (which more or less means “requires” as an accepted practice) static analysis at SIL 2 and above (IEC 61508-3 pg. 83).

Finally, an automotive manufacturer has published data showing that they expect one “major bug” for every 30 coding rule violations (Kawana 2004):


(Figure from Kawana 2004)

Note: As with my other posts in the last few months this was written regarding practices about a decade ago. There are newer sources for coding style information available now such as an updated version of MISRA C. There is also the the ISO-26262 standard, which is intended to replace the MISRA software guidelines..  But we'll save discussing those for another time.

References:

  • Hatton, Les, Safer C, 1995.
  • Jones, N., MISRA C guidelines, Embedded Systems Programming, Beginner’s Corner, July 2002, pp. 55-56.
  • Kawana et al., Empirical Approach for Reliability Assurance of Vehicle Software, Automotive Software Workshop, San Diego, 2004.
  • MISRA, (MISRA C), Guideline for the use of the C Language in Vehicle Based Software, April 1998.
  • MISRA, Development Guidelines for Vehicle Based Software, November 1994 (PDF version 1.1, January 2001).
  • MISRA, Report 2: Integrity, February 1995.
  • Nagappan & Ball, Static analysis tools as early indicators of pre-release defect density, International Conference on Software Engineering, 2005, pp. 580-586.


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