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Finding links between gcc source code and the C Standard

October 19, 2025 2 comments

How close is the agreement between the behavior of a compiler and its corresponding language specification?

In the previous century, some Standards’ bodies offered a compiler validation service. However, even when the number of commercial compilers numbered in the hundreds, this service was not commercially viable. These days there are only a handful of industrial strength compilers.

The availability of huge quantities of Open source, for some languages, has created a new language specification. Being able to turn much of this source into executable programs has become an effective measure of compiler correctness.

Those working on C/C++ compilers (Open source or otherwise), often claim that they implement the requirements contained in the corresponding ISO Standard. Some are active in the ISO Standards’ process, and I believe that they do strive to implement the requirements contained in the language standard.

How confident can we be that all the requirements contained in a language standard are correctly implemented by a compiler?

There is a cottage industry of testing compiler runtime behavior, often using fuzzers, and sometimes a compiler is one of the programs chosen to test new fuzzing techniques. This research checks optimization and code generation.

This runtime testing is all well and good, but a large percentage of the text in a language specification contains requirements on the syntax and semantics. The quality of syntax/semantic testing depends on how well the people writing the tests understand the language semantics. It takes a year or two of detailed study to achieve an effective compiler-level of understanding of these ‘front-end’ requirements.

The approach taken by the Model Implementation C Checker to show syntax/semantic correctness was to cross-referenced every if-statement in the front-end to one or more lines in the C90 Standard (the 1990 edition of the ISO C Standard), or an internal house-keeping reference (the source contained 3K references to 1.3K requirements in the C Standard). This compiler/checker was formally validated by BSI. As far as I know, this is the only compiler source cross-referenced at the level of individual lines/if-statements; there are compilers whose source contains cross-references to the sections of a language specification.

The main benefit of this cross-referencing process is insuring that every requirement in the C Standard is addressed by the compiler (correctly or otherwise). Other benefits include providing packets of wording for targeted tests and the ability to generate a runtime trace of all language features involved in compiling a given translation unit.

Replicating this cross-referencing for the gcc or llvm C compiler front-ends would be a huge amount of work for somebody who already has a detailed knowledge of the C Standard, along with some knowledge of compilers. The number of pages in the Standard relating to the C language has grown from 101 pages in C90 to 190 pages in C23. At an average of 14 cross-referenceable lines per page, the expected number of cross-references is now likely to be around 2,700.

LLMs are great at extracting information from text, can generate impressive quality C conformance tests, and are much, much cheaper than C language experts. How good are LLMs at working out which section(s)/paragraph(s) in the C Standard are implemented by the source code of a function contained in, say, gcc?

The LLM prompt I used to test out the idea was:

   List the section and paragraph number of the latest
   ISO C standard containing the requirements implemented
   in the following source code from a C compiler

Links to all responses from: ChatGPT5 Thinking, Grok3 Fast and Gok3 Expert.

It is very likely that text from the various versions of the C Standard were in the training data of the larger LLMs, so I did not include this material in the prompt. ChatGPT5 Thinking listed the 2024 draft N3220 as its source document.

The source code was copied from the C front-end code of gcc (i.e., the directory: gcc-15.1.0/gcc/c), which contains 67KLOC, with 6.8K if-statements.

Starting small, the following code looked like an easy test (I removed the comment containing text from the C Standard and obscured the big clue in the original function name: null_pointer_constant_p):

bool check_is_n_ptr (const_tree expr)
{
  /* This should really operate on c_expr structures, but they aren't
     yet available everywhere required.  */
  tree type = TREE_TYPE (expr);
 
  if (expr == nullptr_node)
    return true;
 
  return (TREE_CODE (expr) == INTEGER_CST
          && !TREE_OVERFLOW (expr)
          && integer_zerop (expr)
          && (INTEGRAL_TYPE_P (type)
              || (TREE_CODE (type) == POINTER_TYPE
                  && VOID_TYPE_P (TREE_TYPE (type))
                  && TYPE_QUALS (TREE_TYPE (type)) == TYPE_UNQUALIFIED)));
}

Wow, ChatGPT5 Thinking’s response even included a note on why the code checks for an unqualified type:

Here are the exact C23 (ISO/IEC 9899:2024) clauses that your function is implementing:

6.3.2.3 Pointers, ¶3 — Definition of a null pointer constant: “An integer constant expression
   with the value 0, such an expression cast to type void *, or the predefined constant nullptr
   is called a null pointer constant.”
6.6 Constant expressions, ¶8 — What qualifies as an integer constant expression
   (your INTEGER_CST / INTEGRAL_TYPE_P checks).
6.6 Constant expressions, ¶4 — Range requirement for constant expressions
   (motivates the !TREE_OVERFLOW (expr) check).
6.4.4.6 Predefined constants, ¶¶1–3 — nullptr is a predefined constant (token classification;
    ties to your expr == nullptr_node fast-path).
6.3.2.4 nullptr_t, ¶¶1–2 — Conversions of nullptr_t (confirms nullptr converts to any pointer
    type yielding the null pointer value).
Notes
  • The “cast to type void *” wording in 6.3.2.3 means unqualified void *,
    which is why your code insists the pointed-to void is unqualified.

Grok3 Fast’s response was even more expansive (and almost instantaneous), listing examples of section/paragraphs containing uses of null pointers, and pointing out that the wording was unchanged until C23 (when the nullptr keyword was introduced, along with its type nullptr_t). It also pointed out gcc specific features of the implementation (the prompt did not identify the compiler).

That’s enough of the easy questions. The following code (comments removed, function name unchanged) is essentially asking a question: What is the promoted type of the argument?

tree c_type_promotes_to (tree type)
{
  tree ret = NULL_TREE;
 
  if (TYPE_MAIN_VARIANT (type) == float_type_node)
    ret = double_type_node;
  else if (c_promoting_integer_type_p (type))
    {
      if (TYPE_UNSIGNED (type)
          && (TYPE_PRECISION (type) == TYPE_PRECISION (integer_type_node)))
        ret = unsigned_type_node;
      else
        ret = integer_type_node;
    }
 
  if (ret != NULL_TREE)
    return (TYPE_ATOMIC (type)
            ? c_build_qualified_type (ret, TYPE_QUAL_ATOMIC)
            : ret);
 
  return type;
}

ChatGPT5 listed six references. Three were good, and the other three were closely related, but I would not have cited them. The seven Grok3 references came from several documents using slightly different section numbers. Updating the prompt to explicitly name N3220 as the document to use did not change Grok3’s cited references (for this question).

All the code in the previous questions was there because of text in the C Standard. How do ChatGPT5/Grok3 handle the presence of code that does not have standard associated text?

The following function contains code to handle named address spaces (defined in a 2005 Technical Report: TR 18037 Extensions to support embedded processors).

static tree
qualify_type (tree type, tree like)
{
  addr_space_t as_type = TYPE_ADDR_SPACE (type);
  addr_space_t as_like = TYPE_ADDR_SPACE (like);
  addr_space_t as_common;
 
  /* If the two named address spaces are different, determine the common
     superset address space.  If there isn't one, raise an error.  */
  if (!addr_space_superset (as_type, as_like, &as_common))
    {
      as_common = as_type;
      error ("%qT and %qT are in disjoint named address spaces",
             type, like);
    }
 
  return c_build_qualified_type (type,
                        TYPE_QUALS_NO_ADDR_SPACE (type)
                        | TYPE_QUALS_NO_ADDR_SPACE_NO_ATOMIC (like)
                        | ENCODE_QUAL_ADDR_SPACE (as_common));
}

ChatGPT5 listed six good references and pointed out the association between the named address space code and TR 18037. Grok3 Fast hallucinated extensive quoted text/references from TR 18037 related to named address spaces. Grok3 Expert pointed out that the Standard does not contain any requirements related to named address spaces and listed two reasonable references.

Finding appropriate cross-references is the time-consuming first step. Next, I want the LLM to add them as comments next to the corresponding code.

I picked a 312 line function, and updated the prompt to add comments to the attached file:

   Find the section and paragraph numbers in the ISO C
   standard, specified in document N3220, containing the
   requirements implemented in the source code contained
   in the attached file, and add these section and paragraph
   numbers at the corresponding places in the code as comment

ChatGPT5 Thinking thought for 5 min 46 secs (output), and Grok3 Expert thought for 3 mins 4 secs (output).

Both ChatGPT5 and Grok3 modified the existing code, either by joining adjacent lines, changing variable names, or deleting lines. ChatGPT made far fewer changes, while the Grok3 output was 65 lines shorter than the original (including the added comments).

Both LLMs added comments to blocks of if-statements (my fault for not explicitly specifying that every if should be cross-referenced), with ChatGPT5 adding the most cross-references.

One way to stop the LLMs making unasked for changes to the source is to have them focus on the added comments, i.e., ask for a diff that can be fed into patch. The updated prompt is:

   Find the section and paragraph numbers in the ISO C
   standard, specified in document N3220, containing the
   requirements implemented by each if statement in the
   source code contained in the attached file.  Create a
   diff file that patch can use to add these section and
   paragraph numbers as comments at the corresponding lines
   in the original code

ChatGPT5 Thinking thought for around 4 min (it reported inconsistent values (output), and Grok3 Expert thought for 5 min 1 sec (output).

The ChatGPT5 patch contained many more cross-references than its earlier output, with comments on more if-statements. The Grok3 patch was a third the size of the ChatGPT5 patch.

How well did the LLMs perform?

ChatGPT5 did very well, and its patch output would be a good starting point for a detailed human expert edit. Perhaps an improved prompt, or some form of fine-tuning would useful improve performance.

Grok3 Fast does not appear to be usable, but Grok3 Expert could be used as an independent check against ChatGPT5 output.

Working at the section/paragraph level it is not always possible to give the necessary detailed cross-reference because some paragraphs contain multiple requirements. It might be easier to split the C Standard text into smaller chunks, rather than trying to get LLMs to give line offsets within a paragraph.

Modeling the distribution of method sizes

October 12, 2025 No comments

The number of lines of code in a method/function follows the same pattern in the three languages for which I have measurements: C, Java, Pharo (derived from Smalltalk-80).

The number of methods containing a given number of lines is a power law, with an exponent of 2.8 for C, 2.7 for Java and 2.6 for Pharo.

This behavior does not appear to be consistent with a simplistic model of method growth, in lines of code, based on the following three kinds of steps over a 2-D lattice: moving right with probability R, moving up and to the right with probability U, and moving down and to the right with probability D. The start of an if or for statement are examples of coding constructs that produce a U step followed by a D step at the end of the statement; R steps are any non-compound statement. The image below shows the distinct paths for a method containing four statements:


Number of distinct silhouettes for a function containing four statements

For this model, if U < D the probability of returning to the origin after taking n is a complicated expression with an exponentially decaying tail, and the case U = D is a well studied problem in 1-D random walks (the probability of returning to the origin after taking n steps is P(n) approx n^{-1.5}).

Possible changes to this model to more closely align its behavior with source statement production include:

  • include terms for the correlation between statements, e.g., assigning to a local variable implies a later statement that reads from that variable,
  • include context terms in the up/down probabilities, e.g., nesting level.

Measuring statement correlation requires handling lots of special cases, while measurements of up/down steps is easily obtained.

How can U/D probabilities be written such that step length has a power law with an exponent greater than two?

ChatGPT 5 told me that the Langevin equation and Fokker–Planck equation could be used to derive probabilities that produced a power law exponent greater than two. I had no idea had they might be used, so I asked ChatGPT, Grok, Deepseek and Kimi to suggest possible equations for the RU/D probabilities.

The physics model corresponding to this code related problem involves the trajectories of particles at the bottom of a well, with the steepness of the wall varying with height. This model is widely studied in physics, where it is known as a potential well.

Reaching a possible solution involved refining the questions I asked, following suggestions that turned out to be hallucinations, and trying to work out what a realistic solution might look like.

One ChatGPT suggestion that initially looked promising used a Metropolis–Hastings approach, and a logarithmic potential well. However, it eventually dawned on me that U approx (y/{y+1})^a, where y is nesting level, and a some constant, is unlikely to be realistic (I expect the probability of stepping up to decrease with nesting level).

Kimi proposed a model based on what it called algebraic divergence:

R(y)=r/{z(y)},U(y)={u_0y^{1-2/{alpha}}}/{z(y)}, D(y)={d_0y^{1-2/{alpha}}}/{z(y)}

where: z(y) normalises the probabilities to equal one, z(y)=r+u_0y^{1-2/alpha}+d_0y^{1-2/alpha}, u_0 is the up probability at nesting 0, d_0 is the down probability at nesting 0, and alpha is the desired power law exponent (e.g., 2.8).

For C, alpha=2.8, giving R(y)=r/{z(y)},U(y)={u_0y^{0.29}}/{z(y)}, D(y)={d_0y^{0.29}}/{z(y)}

The average length of a method, in LOC, is given by:

E[LOC]={alpha r}/{2(d_0-u_0)}+O(e^{lambda}-1), where: lambda={2(d_0-u_0)}/{d_0+u_0}

For C, the mean function length is 26.4 lines, and the values of r, u_0, and d_0 need to be chosen subject to the constraint r+u_0+d_0=1.

Combining the normalization factor z(y) with the requirement u_0 < d_0, shows that as y increases, U(y) slowly decreases and D(y) slowly increases.

One way to judge how closely a model matches reality is to use it to make predictions about behavior patterns that were not used to create the model. The behavior patterns used to build this model were: function/method length is a power law with exponent greater than 2. The mean length, E[LOC], is a tuneable parameter.

Ideally a model works across many languages, but to start, given the ease of measuring C source (using Coccinelle), this one language will be the focus.

I need to think of measurable source code patterns that are not an immediate consequence of the power law pattern used to create the model. Suggestions welcome.

It’s possible that the impact of factors not included in this model (e.g., statement correlation) is large enough to hide any nesting related patterns that are there. While different kinds of compound statements (e.g., if vs. for) may have different step probabilities, in C, and I suspect other languages, if-statement use dominates (Table 1713.1: if 16%, for 4.6% while 2.1%, non-compound statements 66%).

Early research on economies of scale for computer systems

October 5, 2025 No comments

Before microprocessor cost/performance wiped out (in the early 1990s) other cpu platforms (e.g., mainframes and minis), people argued that computer hardware benefited from economies of scale.

The claimed benefit was more bang for the buck, i.e., more compute for less money.

Checking this claim requires treating pre-microprocessor computer systems and the later microprocessor-based systems as two separate cases, because many of the factors driving costs and performance are very different.

Today’s large microprocessor-based computer systems achieve economies of scale through discounts from bulk purchases and spreading fixed costs across multiple systems. The data is available, and the economic analysis is straight forward.

A lack of reliable data on the costs of designing/building pre-microprocessor computer systems rules out an economic analysis of cost/performance from first principles. The data that was/is available is the cost of computer systems and some indicators of performance (such as instruction timings or benchmarks).

Now, the observed fact that the cost of compute was decreasing over time is unrelated to the claim that the cost of compute decreases as the size of the computer increases.

Assuming a power law relationship between computer cost, C, and size, S, at a point in time, we have: C approx S^a, where a is some constant. Economies of scale occur when: a < 1

In his detailed cost/performance analysis of computers between 1944-1967, Kenneth Knight treated computers launched in the same year as effectively occurring at the same time. He also built a single model, with year included as an explanatory variable, which means the fitted rate of decrease is the same over all years (rather than varying between years).

The plot below uses Knight’s 1953-1961 data, and shows operations per second against seconds per dollar (a confusing combination, but what Knight used), with fitted regression lines for three years using Knight’s model (code and data)

Operations per second vs. Seconds per dollar for computers 1953-1961

The fitted exponent for this form of x/y axis maps to a value which has a < 1, i.e., there are economies of scale.

It so happens that the value of the Knight’s fitted exponent is close to that proposed in a 1953 paper (“High speed arithmetic: The digital computer as a research tool”, no online copy):

  It used to cost one cent to do a multiplication on a
  desk calculator; now it is more like four cents; but
  with these big machines we can do a million in an hour
  for $400, and that means twenty-five multiplications
  for a cent! I believe that there is a fundamental rule,
  which I modestly call Grosch's law, giving added
  economy only as the square root of the increase in
  speed-that is, to do a calculation ten times as cheaply
  you must do it one hundred times as fast.

which did indeed become widely known as Grosch’s law.

Having been given a lucky kick-start by Knight (fitted individually, years are not close to Grosch’s law), checking for agreement with Grosch’s law became a focus for later studies. While various papers highlighted problems with the later data analysis (e.g., the regression techniques and sample noise producing mathematical artifacts), Grosch’s law ceased being a thing because mainframes/minicomputers ceased being a thing.

Did mainframe/mincomputers have economies of scale in the years after Knight’s data? It’s difficult to tell, the publicly available data is too sparse to support reliable analysis.

Data+code for book: The New C Standard

September 28, 2025 No comments

All the data+code from my book The New C Standard: An Economic and Cultural Commentary is now available on GitHub. For many years I have been meaning to create an easy way to map from a graph/table in the book to the file containing the data, which has blocked me adding the data to GitHub. I have unblocked by releasing this minimal viable product, i.e., it is essentially a copy of the usage subdirectory in the book’s directory.

While the five stage process to get from graph/table to data is tedious, at least there is a process that provides the data. The caption of the graphs in my Evidence-based Software Engineering book contain a link to the corresponding file on GitHub. This was not possible for the C book because GitHub was still 3-years in the future when the book was published (in 2005).

Work on the book started in late 1999 and measurements of C usage was an integral component. Publicly available source code was still a novelty and large Open source projects were rare (SourceForge was launched at the end of 1999). The large projects with C source available to measure were: Linux, Netscape, Gcc, PostgresSQL, OpenAFS, and OpenMotif. Several popular projects originally written in C had migrated to using C++, and were therefore not applicable.

As the book was completed in 2005, evidence-based software engineering restarted, 20-years after the fall of Rome. Or rather, I have nominated 2005 as the year this happened. Feel free to quibble plus/minus a few years.

Search engines were an essential tool for obtaining research papers, reports, and occasionally downloading data. In 2000 the search engine of choice was AltaVista, but a few years later Google had become the best.

While writing the book, I was a regular visitor to bricks and mortar buildings called libraries. Back then, university libraries contained tens of thousands of physical books, and researchers would photocopy papers of interest. Little did I know that this research practice would soon be dead.

In 2005, I had this to say about software evolution:

Measuring the characteristics of software that change over many
releases (software evolution) is a relatively new research topic.
Software evolution is discussed in a few sentences, and any
future major revision ought to cover this important topic in
substantially more detail.

How might C source code characteristics have changed in the last 20 years?

  • The use of K&R style function definitions is probably very rare; it was well on the way out in 1999,
  • big software systems have gotten bigger, i.e., more lines of code and more #includes,
  • A lot more code using 32-bit integers and 64-bit pointers,
  • More storage allocated (memory capacity has increased) because it’s faster to do everything in memory, and there is more data.
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Distribution of integer literals in text/speech and source code

September 21, 2025 No comments

Numeric values are an integral to communication between people. What is the distribution of integer values in text/speech, and does the use of integer literals in source code have a similar distribution?

Programs are an implementation of a sliver of the world in which people live, and it is to be expected that the frequency of numeric literal values in source code is highly correlated with real world frequency. Numeric values also appear in the algorithms and mathematical expressions used to create implementations. I am not aware of any studies looking at the frequency of use of numeric constants in algorithms and mathematics. As an aside, the frequency of occurrence of mathematical expressions containing a given number of operators is similar to that in C source

What are the usage characteristics of integer literals in source code (floating-point literal use is very rare outside of particular application domains)?

The plot below shows occurrences of decimal (green) and hexadecimal (blue) literals in C source (data from fig 825.1 from my C book) with a regression line fitted to values 1..50 of the decimal data (code+data):

Occurrences of decimal and hexadecimal literals in C source.

The frequency of decimal literal values in C source is proportional to: value^{-1.3}. Adding the hexadecimal values to the model has little effect.

The paper What do developers consider magic literals? A smalltalk perspective by Anquetil, Delplanque, Ducasse, Zaitsev, Fuhrman, and Guéhéneuc studied the use of literals in Smalltalk. The plot below shows the number of occurrences of all kinds of integer literals and a fitted regression line (code+data):

Occurrences of decimal and hexadecimal literals in Smalltalk source.

The frequency of integer literal values in Smalltalk source is proportional to: value^{-1.7}.

The distribution of integer literals in both human communication and source code is well-fitted by a power law. Smalltalk appears to be the outlier, with an exponent of 1.7 vs 1.3-1.4. Perhaps it’s a sample size issue; 14,054 integer literals for Smalltalk and a million+ for the other datasets.

I had expected source code to contain a lot more zeroes/ones, relative to other values, than human communication. Zero/one are such common values that there are implicit short-cuts that people can use to express them; removing the effort/cost needed to explicitly specify them. Some programming languages specify default 0/1 values for common idioms, but C-like languages generally require explicit specification of values.

ISO C++ committee has a new chief sheep herder

September 14, 2025 2 comments

The ISO C++ Standards committee, WG21, has a new convenor, Guy Davidson, or rather they will have when the term of the current convenor, Herb Sutter, expires at the end of this year.

Apart from the few people directly involved, this appointment does not matter to anybody (sorry Guy). The WG21 juggernaut will continue on its hedonistic way, irrespective of who is currently the chief sheep herder.

Before discussing the evolution of language standards, a brief summary of the unusual points around this appointment:

  1. More than one person volunteered for the job (several in the US, who selected Jeff Garland, and one in the UK; everyone agreed that both were capable candidates). The announcement by a programming language convenor that they are not standing again when their 3-year term expires more commonly kicks off discrete discussions about whose arm can be twisted to take on the role. It’s a thankless task that consumes time and money (to attend extra meetings). Also, the convenor has to be neutral, which circumscribes being involved in technical discussion.

    Sometimes an outsider pops up, ruffles a few feathers and then disappears (from the Standards’ world).

  2. One of the SC22 (the ISO committee responsible for programming languages) convenor selection rules says (see Resolution 14-04): “When a WG Convenorship becomes vacant, … and multiple NBs have each nominated a candidate, the Convenorship shall be assigned to the candidate whose NB currently has the fewest SC 22 Convenors.” Currently, the US holds multiple convenorships and the UK holds none, so the UK nominee is appointed.

    As often happens, people like diversity rules until they lose out. The US submitted a selection procedural change to SC22, and asked that it take effect before the selection of a new WG21 convenor. The overwhelming consensus at the SC22 plenary last Monday was not to change the rules while an election was in progress. An ad-hoc committee was set up to consider changes to the current rules.

End of the news and back to regular postings.

Standards committees for programming languages are now a vestige from a bygone era. The original purpose of standards was to reduce costs (the UK focused on savings achieved through repeated use of standardized items and the US focused on reduced training costs) by having companies manufacture products that conformed to a single specification.

There were once a multitude of implementations for the commercially important languages, each supporting slightly different dialects (the differences were sometimes not so slight). Language standards provided a base specification for developers interested in portable code to keep within, and that vendors could be pressured to support.

The spread of Open source compilers significantly reduced the need for companies to invest in maintaining their own compiler (there might be strategic reasons for companies selling hardware or operating systems to continue to invest in their own compiler), and reduced the likelihood that customers of commercial compiler companies would continue to pay for updates (effectively driving most compiler companies out of business).

Language standards are redundant in a monoculture, i.e., where only one compiler per language is widely used. For some years now, there have been a handful of actively maintained compilers for the widely used languages.

These days, conformance to a language standard is measured by the ability of an implementation to compile and execute the Open source software available in the various ecosystems.

As has often been observed, committees find work to keep themselves busy, and I have seen announcements for new ISO committees that look like they were created because somebody saw a CV padding opportunity.

I continue to think that the C++ committee has become a playground for bored consultants looking for a creative outlet.

WG21 meeting attendance continues to grow, now attracting 200+ attendees (Grok undercounts, e.g., 140 vs 215, and ChatGPT 5 is completely out of its depth). This is an order of magnitude greater than the C committee, WG14, and in a few years could be two orders of magnitude greater than the other SC22 languages.

The two major C/C++ compiler vendors (i.e., gcc and llvm) could simply go their own way, with regard to new language features. However, I imagine that “supporting the latest version of the language standard” is a great rationale to use when asking for funding.

How large can WG21 become before it collapses under the weight of members and the papers they write?

The POSIX standard, WG15, meetings often had 200-300 attendees in the late 1980s/early 1990s. But the POSIX committee stuck to its goal of specifying existing practice, and so has faded away.

Guy strikes me as an efficient administrator. Which is probably bad news, in the sense that this could enable WG21 to grow a lot larger. What ever happens, it will be interesting to watch.

Percentage of methods containing no reported faults

September 7, 2025 No comments

It is often said, with some evidence, that 80% of reported faults, for a program, occur in 20% of its code. I think this pattern is a consequence of 20% of the code being executed 80% of the time, while many researchers believe that 20% of the source code has characteristics that result in it containing 80% of the coding mistakes.

The 20% figure is commonly measured as a percentage of methods/functions, rather than a percentage of lines of code.

This post investigates the expected fraction of a program’s methods that remain fault report free, based on two probability models.

Both models assume that coding mistakes are uniformly scattered throughout the code (i.e., every statement has the same probability of containing a mistake) and that the corresponding coding mistake is contained within a single method (the evidence suggests that this is true for 50% of faults).

A simple model is to assume that when a new fault is reported, the probability that the corresponding coding mistake appears in a particular method is proportional to the method’s length, L in lines of code, of the method. The evidence shows that the distribution of methods containing a given number of lines, L, is well-fitted by a power law (for Java: L^{-2.35}).

If F reported faults have been fixed in a program containing M methods/functions, what is the expected number of methods that have not been modified by the fixing process?

The answer (with help from: mostly Kimi, with occasional help from Deepseek (who don’t have a share chat options), ChatGPT 5, Grok, and some approximations; chat logs) is:

E_m=M/{zeta(b)}Li_b(e^{-{F/M}{{zeta(b)}/{zeta(b-1)}}})

where: zeta is the Riemann zeta function, Li is the polylogarithm function and b=2.35 for Java.

The plot below shows the predicted fraction of unmodified methods against number of faults, for programs of various sizes; the grey lines show the rough approximation: E_m=Me^{-{F/{2M}}} (code+data):

Predicted fraction of unmodified methods against number of reported faults.

The observed behavior of most reported faults involving a subset of a program’s methods can be modelled using some form of preferential attachment.

One preferential attachment model specifies that the likelihood of a coding mistake appearing in a method is proportional to L*(1+R), where R is the number of previously detected coding mistakes in the method.

The estimated number of unmodified methods is now:

E_m=M/{zeta(b)}Li_b(({M zeta(b-1)}/{M zeta(b-1)+a*(F+1) zeta(b)})^{1/a})

where: a is the average value of L*R over all F faults (if R=1, then a=1.74 for a power law with exponent 2.35).

The plot below shows the predicted fraction of unmodified methods against number of faults for a program containing 1,000 methods, for various values of a, with the black line showing the fraction of unmodified methods predicted by the simple model above (code+data):

Predicted fraction of unmodified methods against number of reported faults when likelihood of a modification increases with number of previous modifications.

In practice, random selection of the method containing a coding mistake will introduce some fuzziness in the predicted fraction of unmodified methods.

As the number of reported faults grows, the attraction of methods involved in previous reported faults slows the rate at which methods experience their first detected coding mistake.

How realistic are these models?

By focusing on the number of unmodified methods, many complications are avoided.

Both models assume that an unchanging number of methods in a program and that the length of each method is fixed. This assumption holds between each release of a program.

For actively maintained programs, the number of methods in a program changes over time, and the length of some existing methods also changes (if a program were not actively maintained, reported faults would not get fixed).

These models are unlikely to be applicable to programs with short release cycles, where there are few reported faults between releases.

How well do the models’ predictions agree with the data?

At the moment, I am not aware of a dataset containing the appropriate data. Number of faults vs unmodified methods has been added to my list of interesting patterns to notice.

Summary of the derivation of the solutions for the two models.

Simple model

The expected number of unmodified methods, E(m_u), is:

E(m_u)=sum{L=1}{T}{m_L{P(U_LF)}}, where T is the length of the longest method, m_L is the number of methods of length L, and P(U_LF) is the probability that a method of length L will be unmodified after F fault reports.

The evidence shows that the distribution of methods containing a given number of lines, L, is well-fitted by a power law (for Java: L^{-2.35}).

Given a program containing M methods, the number of methods of length L is:

m_L=M*{L^{-b}/{sum{L=1}{T}{L^{-b}}}}, where b=2.35 for Java.

If T is large and 1<b, then the sum can be approximated by the Riemann zeta function, zeta, giving:

m_L=M*{L^{-b}/{zeta(b)}}

The probability that a method containing L lines will not be modified by a fault report (assuming that fixing the mistake only involves one method) is: 1-L/{P_t}, where P_t is the total lines of code in the program, and the probability of this method not being modified after F fault reports is approximately:

{1-L/{P_t})^F approx e^{{-F*L}/{P_t}}

The expected number of empty boxes is:

E=sum{L=1}{T}{m_L*e^{{-F*L}/{P_t}}}=sum{L=1}{T}{M*{L^{-b}/{zeta(b)}}*e^{{-F*L}/{P_t}}}=M/{zeta(b)}Li_b(e^{-F/{P_t}})

The number of lines of code in a program containing M methods is:

P_t=sum{L=1}{T}{L*m_L}=sum{L=1}{T}{L*M*{L^{-b}/{zeta(b)}}}=M/{zeta(b)}sum{L=1}{T}{L^{1-b}}=M{{zeta(b-1)}/{zeta(b)}}

Finally giving:

E=M/{zeta(b)}Li_b(e^{-{F/M}{{zeta(b)}/{zeta(b-1)}}})

where Li is the polylogarithm function.

This equation is roughly, for the purposes of understanding the effect of each variable:

E=Me^{-{F/{2M}}}

Preferential attachment model

When a mistake is corrected in a method, the attraction weight of that method increases (alternatively, the attraction weight of the other methods decreases). The probability that a method is not modified after F fault reports is now:

prod{k=0}{F}{(1-L/{P_t+a*k})}=prod{k=0}{F}{{P_t+a*k-L}/{P_t+a*k}}={Gamma({P_t}/a)Gamma({P_t-L}/a+F+1)}/{Gamma({P_t-L}/a)Gamma(P_t/a+F+1)}

where: a=sum{i=1}{F}{L_i*R}/F the average value of L*R over all F faults, and Gamma is the gamma function.

applying the Stirling/Gamma–ratio rule, i.e., {Gamma(z+a)}/{Gamma(z+b)} approx z^{a-b} we get:

(P_t/{P_t+a*(F+1)})^{F/a} = ((P_t/{P_t+a*(F+1)})^{1/a})^F

where the expression ((...)^{1/a})^F is the preferential attachment version of the expression {1-L/{P_t})^F appearing in the simple model derivation. Using this preferential attachment expression in the analysis of the simple model, we get:

E_m=M/{zeta(b)}Li_b(({M zeta(b-1)}/{M zeta(b-1)+a*(F+1) zeta(b)})^{1/a})

I don’t have a rough approximation for this expression.

Halstead/McCabe: a complicated formula for LOC

August 31, 2025 No comments

My experience is that people prefer to ignore the implications of Halstead’s metric and McCabe’s complexity metric being strongly correlated (non-linearly) with lines of code (LOC). The implications being that they have been deluding themselves and perhaps wasting time/money using Halstead/McCabe when they could just as well have used LOC.

If the purpose of collecting metrics is a requirement to tick a box, then it does not really matter which metrics are collected. The Halstead/McCabe metrics have a strong brand, so why not collect them.

Don’t make the mistake of thinking that Halstead/McCabe is more than a complicated way of calculating LOC. This can be shown by replacing Halstead/McCabe by the corresponding LOC value to find that it makes little difference to the value calculated.

Some metrics include the Halstead metrics and/or the McCabe metric as part of their calculation. The Maintainability Index is a metric calculated using Halstead’s volume, McCabe’s complexity and lines of code. Its equation is (see below for details):

MI=171-5.2*ln(HalsteadVolume)-0.23*McCabe-16.2*ln(LOC)

Replacing the Halstead/McCabe terms by one involving just LOC requires an appropriate mapping. Nearly all researchers assume a linear mapping, despite the overwhelming evidence that the mapping is non-linear.

Fitting regression models for HalsteadVolume vs LOC and McCabe vs LOC, using measurements of 730K methods from 47 Java projects (see below for data details), produces the coefficients for the equation needed to map each metric to LOC (previous analysis has found that a power law provides the best mapping; code+data). Substituting these equations in the Maintainability Index equation above, we get:

locMI=171-5.2*(2.9+1.2*ln(LOC))-0.23*(0.45*LOC^{0.71})-16.2*ln(LOC)

which simplifies to:

locMI=155.91-22.6*ln(LOC)-0.1*LOC^{0.71}

How does the value calculated using MI compare with the corresponding locMI value?

For 99.7% of methods, the relative error, delim{|}{locMI-MI}{|}/MI, for the 730K Java methods is less than 10%, and for 98.6% of methods the relative error is less than 5% (code+data).

Given the fuzzy nature of these metrics, 10% is essentially noise.

Looking at the relative contributions made by Halstead/McCabe/LOC to the value of the Maintainability Index, second equation above, the Halstead contribution is around a third the size of the LOC contribution and the McCabe contribution is at least an order of magnitude smaller.

Background on the Maintainability Index and the measured Java projects.

The Maintainability Index was introduced in the 1994 paper “Construction and Testing of Polynomials Predicting Software Maintainability” by Oman, and Hagemeister (270 citations; no online pdf), a 1992 paper by the same authors is often incorrectly cited (426 citations). The earlier 1992 paper identified 92 known maintainability attributes, along with 60 metrics for “… gauging software maintainability …” (extracted from 35 published papers).

This Maintainability Index equation was chosen from “Approximately 50 regression models were constructed and tested in our attempts to identify simple models that could be calculated from existing tools and still be generic enough to be applied to a wide range of software.” The data fitted came from eight suites of programs (average LOC 3,568 per suite), along “… with subjective engineering assessments of the quality and maintainability of each set of code.”

Yes, choosing from 50 regression models looks like overfitting, and by today’s standards 28.5K LOC is a tiny amount of source.

The data used is distributed with the paper Revisiting the Debate: Are Code Metrics Useful for Measuring Maintenance Effort? by Chowdhury, Holmes, Zaidman, and Kazman, which does a good job of outlining the many different definitions of maintenance and the inconsistent results from prediction models. However, the authors remain under the street light of project source code, i.e., they ignore the fact that many maintenance requests are driven by demand for new features.

The authors investigate the impact of normalizing Halstead/McCabe by LOC, but make the common mistake of assuming a linear relationship. They are surprised by the high correlation between post-‘normalised’ Halstead/McCabe and LOC. The correlation disappears when the appropriate non-linear normalization is used; see code+data.

A 2014 paper by Najm also maps the components of the Maintainability Index to LOC, but uses a linear mapping from the Halstead/McCabe terms to LOC, creating a locMI equation whose behavior is noticeably different.

Half-life of Open source research software projects

August 24, 2025 No comments

The evidence for applications having a half-life continues to spread across domains. The first published data covered IBM mainframe applications up to 1992 (half-life of at least 5-years), and was mostly ignored. Then, the data collected by Killed by Google up to 2018, showed a half-life of at least 3-years for Google apps. More recently, the data collected by Killed by Microsoft up to 2025, showed a half-life of at least 7-years for Microsoft apps (perhaps reflecting the maturity of the company’s product line).

The half-life of source code, independent of the lifetime of the application it implements, is a separate topic.

Scientific software created to support researchers is an ecosystem whose incentives and means of production can be very different from commercial software. Does researcher oriented software die when the grant money runs out, or the researcher moves on to the next fashionable topic, or does it live on as the field expands?

The paper Scientific Open-Source Software Is Less Likely to Become Abandoned Than One Might Think! Lessons from Curating a Catalog of Maintained Scientific Software by Thakur, Milewicz, Jahanshahi, Paganini, Vasilescu, and Mockus analysed 14,418 scientific software systems written in Python (53%), C/C++ (25%), R (12%), Java (8%) or Fortran (2%). The first half of the paper describes how World of Code‘s 209 million repos were filtered down to 350,308 projects containing README files, these READMEs were processed by LLMs to extract information and further filter out projects.

The authors collected the usual information about each Open source project, e.g., number of core developers, number of commits, programming language, etc. They also collected information about the research domain, e.g., scientific field (biology, chemistry, mathematics, etc.), funding, academic/government associations, etc. A Cox proportional hazards model was fitted to this data, with project lifetime being the response variable. A project was deemed to have been abandoned when no changes had been made to the code for at least six consecutive months (we can argue over whether this is long enough).

Including all the different factors created a Cox model that did a good job of explaining the variance in project survival rate. No one factor dominated, and there was a lot of overlap in the confidence bounds of the components of each factor, e.g., different research domains. I have always said that programming language has no impact on project lifetime; the language factor of the fitted model was not statistically significant (two of the languages just sneaked in under the 5% bar), which can be interpreted as being consistent with my opinion.

Each project was categorised as one of: Scientific Domain-specific code (73.5%), Scientific infrastructure (16.5%), or Publication-Specific code (10%). The plot below shows the Kaplan-Meier survival curve for these three categories (note: y-axis is logarithmic), with faint grey lines showing a fitted exponential for each survival curve (only 3% of projects are abandoned in the first year, and the exponential fits are to the data after the first year; code+data):

Survival rate of software projects in various categories, with fitted exponentials.

Readers familiar with academic publishing will not be surprised that projects associated with published papers have the lowest survival rate (half-life just over 2-years). Infrastructure projects are likely to be depended on by many people, who all have an interest in them surviving (half-life around 6-years). The Domain-specific half-life is around 4.5-years.

The results of this study show software systems in various research ecosystems having a range of half-lives in the same range as three major commercial software ecosystems.

Unfortunately, my experience of discussing application half-life with developers is that they believe in an imagined future where software never dies. That is, they are unwilling to consider a world where software has a high probability of being abandoned, because it requires that they consider the return on investment before spending time polishing their code.

Positive and negative descriptions of numeric data

August 17, 2025 No comments

Effective human communication is based on the cooperative principle, i.e., listeners and speakers act cooperatively and mutually accept one another to be understood in a particular way. However, when seeking to present a particular point of view, speakers may prefer to be economical with the truth.

To attract citations and funding, researchers sell their work via the papers they publish (or blogs they write), and what they write is not subject to the Advertising Standards Authority rule that “no marketing communication should mislead, or be likely to mislead, by inaccuracy, ambiguity, exaggeration, omission or otherwise” (my default example).

When people are being economical with the truth, when reporting numeric information, are certain phrases or words more likely to be used?

The paper: Strategic use of English quantifiers in the reporting of quantitative information by Silva, Lorson, Franke, Cummins and Winter, suggests some possibilities.

In an experiment, subjects saw the exam results of five fictitious students and had to describe the results in either a positive or negative way. They were given a fixed sentence and had to fill in the gaps by selecting one of the listed words; as in the following:

             all                      all
             most                     most                  right
In this exam .... of the students got .... of the questions .....
             some                     some                  wrong
             none                     none

If you were shown exam results with 2 out of 5 students failing 80% of questions and the other 3 out of 5 passing 80% of questions, what positive description would you use, and what negative description would you use?

The 60 subjects each saw 20 different sets of exam results for five fictitious students. The selection of positive/negative description was random for each question/subject.

The results found that when asked to give a positive description, most responses focused on questions that were right, and when asked to give a negative description, most responses focused on questions that were wrong

How many questions need to be answered correctly before most can be said to be correct? One study found that at least 50% is needed.

“3 out of 5 passing 80%” could be described as “… most of the students got most of the questions right.”, and “2 out of 5 students failing 80%” could be described as “… some of the students got most of the questions wrong.”

The authors fitted a Bayesian linear mixed effect models, which showed a somewhat complicated collection of connections between quantifier use and exam results. The plots below provide a visual comparison of the combination of quantifier use for positive (upper) and negative (lower) descriptions.

The alluvial plot below shows the percentage flow, for Positive descriptions, of each selected quantifier through student and question, and then adjective (code+data):

Alluvial plot of quantifiers and adjective used in positive descriptions of exam results.

For the same distribution of exam results, the alluvial plot below shows the percentage flow, for Negative descriptions, of each selected quantifier through student and question, and then adjective (code+date):

Alluvial plot of quantifiers and adjective used in negative descriptions of exam results.

Other adjectives could be used to describe the results (e.g., few, several, many, not many, not all), and we will have to wait for the follow-up research to this 2024 paper.