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if statement conditions, some basic measurements
The conditions contained in if-statements control all the decisions a program makes, yet relatively little is known about their characteristics.
A condition contains one or more clauses, for instance, the condition (a && b) contains two clauses that both need to be true, for the condition to be true. An earlier post modelled the number of clauses in Java conditions, and found an exponential decline (around 90% of conditions contained a single clause, for C this is around 85%).
The condition in a nested if-statement contains implicit decisions, because its evaluation depends on the conditions evaluated by its outer if-statements. I have long predicted that, on average, the number of clauses in a condition will decrease as if-statement nesting increases, because some decisions are subsumed by outer conditions. I have not seen any measurements on conditionals vs nesting, and this week this question reached the top of my to-do list.
I used Coccinelle to extract the text contained in each condition, along with the start/end line numbers of the associated if/else compound statement(s). After almost 20 years, Coccinelle is still the most flexible C source analysis tool available that does not require delving into compiler internals. The following is an example of the output (code and data):
file;stmt;if_line;if_col;cmpd_end;cmpd_line_end;expr sqlite-src-3460100/src/fkey.c;if;240;10;240;243;aiCol sqlite-src-3460100/src/fkey.c;if;217;6;217;217;! zKey sqlite-src-3460100/src/fkey.c;if;275;8;275;275;i == nCol sqlite-src-3460100/src/fkey.c;if;1428;6;1428;1433;aChange == 0 || fkParentIsModified ( pTab , pFKey , aChange , bChngRowid ) sqlite-src-3460100/src/fkey.c;if;808;4;808;808;iChildKey == pTab -> iPKey && bChngRowid sqlite-src-3460100/src/fkey.c;if;452;4;452;454;nIncr > 0 && pFKey -> isDeferred == 0 |
The conditional expressions (last column above) were reduced to a basic form involving simple variables and logical operators, along with operator counts. Some example output below (code and data):
simp_expr,land,lor,ternary v1,0,0,0 v1 && v2,1,0,0 v1 || v2,0,1,0 v1 && v2 && v3,2,0,0 v1 || ( v2 && v3 ),1,1,0 ( v1 && v2 ) || ( v3 && v4 ),2,1,0 ( v1 ? dm1 : dm2 ),0,0,1 |
The C source code projects measured were the latest stable versions of Vim (44,205 if-statements), SQLite (27,556 if-statements), and the Linux kernel (version 6.11.1; 1,446,872 if-statements).
A side note: I was surprised to see the ternary operator appearing in some conditions; in effect, an if within an if (see last line of the previous example). The ternary operator usually appears as a component of a large conditional expression (e.g., x + ( v1 ? dm1 : dm2 ) > y), rather than itself containing clauses, e.g., ( v1 ? dm1 : dm2 ) && v2. I have not seen the requirements for this operator discussed in any analysis of MC/DC.
The plot below shows the number of if-statements occurring at a given nesting level, along with regression fits, of the form 
, to the Vim and SQLite data; the Linux data was better fit by a power law (code+data):
I suspect that most of the deeply nested levels in Vim and SQLite are the results of long else if chains, which, while technically highly nested, could all have been written having the same nesting level, such as the following:
if (strcmp(x, "abc")) ; // code else if (strcmp(x, "xyz")) ; // code else if (strcmp(x, "123")) ; // code |
This if else pattern does not appear to be common in Linux. Perhaps ‘regularizing’ the if else sequences in Vim and SQLite will move the distribution towards a power law (i.e., like Linux).
Average nesting depth will also be affected by the average number of lines per function, with functions containing more statements providing the opportunity for more deeply nested if-statements (rather than calling a function containing nested if-statements).
The plot below shows the number of occurrences of conditions containing a given number of clauses. Neither the exponential and power law are good fits, and log-log axis are used because it shows the points are closer to forming a straight line (code+data):
The plot below shows the nesting level and number of clauses in the condition for each of the 1,446,872 if-statements in the Linux kernel. Each value was ‘jittered’ to distribute points about their actual value, creating a more informative visualization (code+data):
As expected, the likelihood of a condition containing multiple clauses does decrease with nesting level. However, with around 85% of conditions containing a single clause, the fitted regression models essential predict one clause for all nesting levels.
Measuring non-determinism in the Linux kernel
Developers often assume that it’s possible to predict the execution path a program will take, for a given set of input values, i.e., program behavior is deterministic. The execution path may be very complicated, and may depend on the contents of certain files (e.g., SQL engines), but it’s deterministic.
There is one kind of program where determinism is not an option; operating systems are non-deterministic when running in a mode where interrupts can occur.
How much non-determinism can occur in, say, Linux? For instance, when a program calls a system function (e.g., open, read, write, close), how often does the execution sequence follow the function call tree that appears in the source code, and how many different call sequences actually occur during program execution (because of diversions caused by an interrupt; ignoring control flow within functions)?
A study by Imanol Allende ran the same program 500K+ times, and traced every function call that occurred within the Linux kernel (thanks to Imanol for sending me the data and answering my questions). The program used appears below; the system calls traced were open (two distinct calls), read, write, and close (two distinct calls); a total of six system calls.
// #includes omitted int main(int argc, char **argv) { unsigned char result; int fd1, fd2, ret; char res_str[10]={0}; fd1 = open("/dev/urandom", O_RDONLY); fd2 = open("/dev/null", O_WRONLY); ret = read(fd1 , &result, 1); sprintf(res_str ,"%d", result); ret = write(fd2, res_str, strlen(res_str)); close(fd1); close(fd2); return ret; } |
Analysing each of these six distinct calls, in around 98% of program runs, each call follows the same sequence of function calls within the kernel (the common case for write involves a chain of around 10 function calls). During the other 2’ish% of calls, the common sequence was interrupted for some reason, and the logged call trace includes additional called functions, e.g., calls involving the Read, Copy, Update synchronization mechanism. The plot below shows the growth in the number of unique traces against the number of program runs (436,827 of them) for the close(fd2) call; a fitted regression line is in red, with the first 1,000 runs not included in the fit (code+data):
The fitted regression model is 
, suggesting that the growth in unique traces is slowing (this equation peaks at around 
), while the model fitted to some of the system calls implies ever continuing growth.
Allende investigated more sophisticated techniques for estimating the total number of unique traces, including: extreme value theory and species estimation techniques from ecology.
While around 98% of traces are the common case, over half of the unique traces occurred once in 436,827 runs. The plot below shows the number of occurrences of each unique trace, for the close(fd2) call, with an attempted fit of a bi-exponential model (in red; code+data):
The analysis above looked at one system call, the program contains six system calls. If, for each system call, the probability of the most common trace is 98%, then the probability of all six calls following their respective common case is 89%. As the number of distinct system calls made by a program goes up, the global common case becomes less common, and the number of distinct program traces increases multiplicatively.
Survival of CVEs in the Linux kernel
Software contained in safety related applications has to have a very low probability of failure.
How is a failure rate for software calculated?
The people who calculate these probabilities, or at least claim that some program has a suitably low probability, don’t publish the details or make their data publicly available.
People have been talking about using Linux in safety critical applications for over a decade (multiple safety levels are available to choose from). Estimating a reliability for the Linux kernel is a huge undertaking. This post is taking a first step via a broad brush analysis of a reliability associated dataset.
Public fault report logs are a very messy data source that needs a lot of cleaning; they don’t just contain problems caused by coding mistakes, around 50% are requests for enhancement (and then there is the issue of multiple reports having the same root cause).
CVE number are a curated collection of a particular kind of fault, i.e., information-security vulnerabilities. Data on 2,860 kernel CVEs is available. The data includes the first released version of the kernel containing the problem, and the version of the fixed release. The plot below shows the survival curve for kernel CVEs, with confidence intervals in blue/green (code+data):
The survival curve appears to have two parts: the steeper decline during the first 1,000 days, followed by a slower, constant decline out to 16+ years.
Some good news is that many of these CVEs are likely to be in components that would not be installed in a safety critical application.
Predicting the size of the Linux kernel binary
How big is the binary for the Linux kernel? Depending on the value of around 15,000 configuration options, the size of the version 5.8 binary could be anywhere between 7.3Mb and 2,134 Mb.
Who is interested in the size of the Linux kernel binary?
We are not in the early 1980s, when memory for a desktop microcomputer often topped out at 64K, and software was distributed on 360K floppies (720K when double density arrived; my companies first product was a code optimizer which reduced program size by around 10%).
While desktop systems usually have oodles of memory (disk and RAM), developers targeting embedded systems seek to reduce costs by minimizing storage requirements, security conscious organizations want to minimise the attack surface of the programs they run, and performance critical systems might want a kernel that fits within a processors’ L2/L3 cache.
When management want to maximise the functionality supported by a kernel within given hardware resource constraints, somebody gets the job of building kernels supporting various functionality to find out the size of the binaries.
At around 4+ minutes per kernel build, it’s going to take a lot of time (or cloud costs) to compare lots of options.
The paper Transfer Learning Across Variants and Versions: The Case of Linux Kernel Size by Martin, Acher, Pereira, Lesoil, Jézéquel, and Khelladi describes an attempt to build a predictive model for the size of the kernel binary. This paper includes an extensive list of references.
The author’s approach was to first obtain lots of kernel binary sizes by building lots of kernels using random permutations of on/off options (only on/off options were changed). Seven kernel versions between 4.13 and 5.8 were used, producing 243,323 size/option setting combinations (complete dataset). This data was used to train a machine learning model.
The accuracy of the predictions made by models trained on a single kernel version were accurate within that kernel version, but the accuracy of single version trained models dropped dramatically when used to predict the binary size of later kernel versions, e.g., a model trained on 4.13 had an accuracy of 5% MAPE predicting 4.13, when predicting 4.15 the accuracy is 20%, and 32% accurate predicting 5.7.
I think that the authors’ attempt to use this data to build a model that is accurate across versions is doomed to failure. The rate of change of kernel features (whose conditional compilation is supported by one or more build options) supported by Linux is too high to be accurately modelled based purely on information of past binary sizes/options. The plot below shows the total number of features, newly added, and deleted features in the modelled version of the kernel (code+data):
What is the range of impacts of each build option, on binary size?
If each build option is independent of the others (around 44% of conditional compilation directives in the kernel source contain one option), then the fitted coefficients of a simple regression model gives the build size increment when the corresponding option is enabled. After several cpu hours, the 92,562 builds involving 9,469 options in the version 4.13 build data were fitted. The plot below shows a sorted list of the size contribution of each option; the model 
is 0.72, i.e., quite a good fit (code+data):
While the mean size increment for an enabled option is 75K, around 40% of enabled options decreases the size of the kernel binary. Modelling pairs of options (around 38% of conditional compilation directives in the kernel source contain two options) will have some impact on the pattern of behavior seen in the plot, but given the quality of the current model ( is 0.72) the change is unlikely to be dramatic. However, the simplistic approach of regression fitting the 90 million pairs of option interactions is not practical.
What might be a practical way of estimating binary size for any kernel version?
The size of a binary is essentially the quantity of code+static data it contains.
An estimate of the quantity of conditionally compiled source code dependent on a given option is likely to be a good proxy for that option’s incremental impact on binary size.
It’s trivial to scan source code for occurrences of options in conditional compilation directives, and with a bit more work, the number of lines controlled by the directive can be counted.
There has been a lot of evidence-based research on software product lines, and feature macros in particular. I was expecting to find a dataset listing the amount of code controlled by build options in Linux, but the data I can find does not measure Linux.
The Martin et al. build data is perfectfor creating a model linking quantity of conditionally compiled source code to change of binary size.
Analysis of a subset of the Linux Counter data
The Linux Counter project was started in 1993, with the aim of tracking the growth of Linux users (the kernel was first released two years earlier). Anybody could register any of their machines running Linux; a user ran a script that gathered basic information about a machine, and the output was emailed to the project. Once registered, users received an annual reminder to update information in their entry (despite using Linux since before the 1.0 release, user #46406 didn’t register until 2001).
When it closed (reopened/closed/coming back) it had 120K+ registered users. That’s a lot of information about computers, which unfortunately is not publicly available. I have not had any replies to my emails to those involved, asking for a copy that could be released in anonymized form.
This week I found 15,906 rows of what looks like a subset of the Linux counter data, most entries are post-2005. What did I learn from this data?
An obvious use is the pattern to check is changes over time. While the data does not include any explicit date, it does include the Kernel version, from which the earliest date can be inferred.
An earlier post used SPEC data to estimate the growth in installed memory over time; it has been doubling every 840 days, give or take. That data contains one data point per distinct vendor computer; the Linux counter data contains one entry per computer in use. There is around thirty pairs of entries for updated systems, i.e., a user updated the entry for an existing system.
The plot below shows memory installed in each registered computer, over time, for servers, laptops and workstations, with fitted regression lines. The memory size doubling times are: servers 4,000 days, laptops 2,000 days, and workstations 1,300 days (code+data):
A regression model using dates is a good fit in the statistical sense, but explain very little of the variance in the data. The actual date on which the memory size was selected may have been earlier (because the kernel has been updated to a later release), or later (because memory was added, but the kernel was not updated).
Why is the memory doubling time so long?
Has memory size now reached the big-enough boundary, do Linux counter users keep the same system for many years without upgrading, are Linux counter systems retired Windows boxes that have been repurposed (data on installed memory Windows boxes would answer this point)?
Suggestions welcome.
When memory capacity is limited, it may be useful to swap least recently used memory contents to disc; Linux setup includes the specification of a swap partition. What is the optimal size of the size partition? A common recommendation is: if memory is less than 2G swap size is twice memory; if between 2-8G swap size is the same as memory, and for greater than 8G, half of memory size. The table below shows the percentage of particular system classes having a given swap/memory ratio (rounding the list of ratios to contain one decimal digit produces a list of over 100 ratio values).
swap/memory Server Workstation Laptop 1.0 15.2 19.9 25.9 2.0 10.3 9.6 8.6 0.5 9.5 7.7 8.4 |
The plot below shows memory against swap partition size, for the system classes laptop, server and workstation, with fitted regression line (code+data):
The available disk space also has a (small) impact on swap partition size; the following model explains 46% of the variance in the data: 
.
I was hoping to confirm the rate of installed memory growth suggested by the SPEC data, with installed systems lagging a few years behind the latest releases. This Linux counter data tells a very different growth story. Perhaps pre-2005 data will tell another story (I just need to find it).
I’m not sure if the swap/memory ratio analysis is of any use to systems people. It was something of a fishing expedition on my part.
Other counting projects have included the Ubuntu counter project, and Hardware for Linux which is still active and goes back to August 2014.
I’m interested in hearing about the availability of any other Linux counter data, or data from other computer counting projects.
Linux has a sleeper agent working as a core developer
The latest news from Wikileaks, that GCHQ, the UK’s signal intelligence agency, has a sleeper agent working as a trusted member of the Linux kernel core development team should not come as a surprise to anybody.
The Linux kernel is embedded as a core component inside many critical systems; the kind of systems that intelligence agencies and other organizations would like full access.
The open nature of Linux kernel development makes it very difficult to surreptitiously introduce a hidden vulnerability. A friendly gatekeeper on the core developer team is needed.
In the Open source world, trust is built up through years of dedicated work. Funding the right developer to spend many years doing solid work on the Linux kernel is a worthwhile investment. Such a person eventually reaches a position where the updates they claim to have scrutinized are accepted into the codebase without a second look.
The need for the agent to maintain plausible deniability requires an arm’s length approach, and the GCHQ team made a wise choice in targeting device drivers as cost-effective propagators of hidden weaknesses.
Writing a device driver requires the kinds of specific know-how that is not widely available. A device driver written by somebody new to the kernel world is not suspicious. The sleeper agent has deniability in that they did not write the code, they simply ‘failed’ to spot a well hidden vulnerability.
Lack of know-how means that the software for a new device is often created by cutting-and-pasting code from an existing driver for a similar chip set, i.e., once a vulnerability has been inserted it is likely to propagate.
Perhaps it’s my lack of knowledge of clandestine control of third-party computers, but the leak reveals the GCHQ team having an obsession with state machines controlled by pseudo random inputs.
With their background in code breaking, I appreciate that GCHQ have lots of expertise to throw at doing clever things with pseudo random numbers (other than introducing subtle flaws in public key encryption).
What about the possibility of introducing non-random patterns in randomised storage layout algorithms (he says, waving his clueless arms around)?
Which of the core developers is most likely to be the sleeper agent? His codename, Basil Brush, suggests somebody from the boomer generation, or perhaps reflects some personal characteristic; it might also be intended to distract.
What steps need to be taken to prevent more sleeper agents joining the Linux kernel development team?
Requiring developers to provide a record of their financial history (say, 10-years worth), before being accepted as a core developer, will rule out many capable people. Also, this approach does not filter out ideologically motivated developers.
The world may have to accept that intelligence agencies are the future of major funding for widely used Open source projects.
Update
Turns out the sleeper agent was working on xz.
Predicting the future with data+logistic regression
Predicting the peak of data fitted by a logistic equation is attracting a lot of attention at the moment. Let’s see how well we can predict the final size of a software system, in lines of code, using logistic regression (code+data).
First up is the size of the GNU C library. This is not really a good test, since the peak (or rather a peak) has been reached.
We need a system that has not yet reached an easily recognizable peak. The Linux kernel has been under development for many years, and lots of LOC counts are available. The plot below shows a logistic equation fitted to the kernel data, assuming that the only available data was up to day: 2,900, 3,650, 4,200, and 5,000+. Can you tell which fitted line corresponds to which number of days?
The underlying ‘problem’ is that we are telling the fitting software to fit a particular equation; the software does what it has been told to do, and fits a logistic equation (in this case).
A cubic polynomial is also a great fit to the existing kernel data (red line to the left of the blue line), and this fitted equation can be extended into future (to the right of the blue line); dotted lines are 95% confidence bounds. Do any readers believe the future size of the Linux kernel predicted by this cubic model?
Predicting the future requires lots of data on the underlying processes that drive events. Modeling events is an iterative process. Build a model, check against reality, adjust model, rinse and repeat.
If the COVID-19 experience trains people to be suspicious of future predictions made by models, it will have done something positive.
2019 in the programming language standards’ world
Last Tuesday I was at the British Standards Institute for a meeting of IST/5, the committee responsible for programming language standards in the UK.
There has been progress on a few issues discussed last year, and one interesting point came up.
It is starting to look as if there might be another iteration of the Cobol Standard. A handful of people, in various countries, have started to nibble around the edges of various new (in the Cobol sense) features. No, the INCITS Cobol committee (the people who used to do all the heavy lifting) has not been reformed; the work now appears to be driven by people who cannot let go of their involvement in Cobol standards.
ISO/IEC 23360-1:2006, the ISO version of the Linux Base Standard, has been updated and we were asked for a UK position on the document being published. Abstain seemed to be the only sensible option.
Our WG20 representative reported that the ongoing debate over pile of poo emoji has crossed the chasm (he did not exactly phrase it like that). Vendors want to have the freedom to specify code-points for use with their own emoji, e.g., pineapple emoji. The heady days, of a few short years ago, when an encoding for all the world’s character symbols seemed possible, have become a distant memory (the number of unhandled logographs on ancient pots and clay tablets was declining rapidly). Who could have predicted that the dream of a complete encoding of the symbols used by all the world’s languages would be dashed by pile of poo emoji?
The interesting news is from WG9. The document intended to become the Ada20 standard was due to enter the voting process in June, i.e., the committee considered it done. At the end of April the main Ada compiler vendor asked for the schedule to be slipped by a year or two, to enable them to get some implementation experience with the new features; oops. I have been predicting that in the future language ‘standards’ will be decided by the main compiler vendors, and the future is finally starting to arrive. What is the incentive for the GNAT compiler people to pay any attention to proposals written by a bunch of non-customers (ok, some of them might work for customers)? One answer is that Ada users tend to be large bureaucratic organizations (e.g., the DOD), who like to follow standards, and might fund GNAT to implement the new document (perhaps this delay by GNAT is all about funding, or lack thereof).
Right on cue, C++ users have started to notice that C++20’s added support for a system header with the name version, which conflicts with much existing practice of using a file called version to contain versioning information; a problem if the header search path used the compiler includes a project’s top-level directory (which is where the versioning file version often sits). So the WG21 committee decides on what it thinks is a good idea, implementors implement it, and users complain; implementors now have a good reason to not follow a requirement in the standard, to keep users happy. Will WG21 be apologetic, or get all high and mighty; we will have to wait and see.
Popularity of Open source Operating systems over time
Surveys of operating system usage trends are regularly published and we get to read about how the various Microsoft products are doing and the onward progress of mobile OSs; sometimes Linux gets an entry at the bottom of the list, sometimes it is just ‘others’ and sometimes it is both.
Operating systems are pervasive and a variety of groups actively track reported faults in order to issue warnings to the public; the volume of OS fault informations available makes it an obvious candidate for testing fault prediction models (e.g., how many faults will occur in a given period of time). A very interesting fault history analysis of OpenBSD in a paper by Ozment and Schechter recently caught my eye and I wondered if the fault time-line could be explained by the time-line of OpenBSD usage (e.g., more users more faults reported). While collecting OS usage information is not the primary goal for me I thought people would be interested in what I have found out and in particular to share the OS usage data I have managed to obtain.
How might operating system usage be measured? Analyzing web server logs is an obvious candidate method; when a web browser requests information many web servers write information about the request to a log file and this information sometimes includes the name of the operating system on which the browser is running.
Other sources of information include items sold (licenses in Microsoft’s case, CDs/DVD’s for Open source or perhaps books {but book sales tend not to be reported in the way programming language book sales are reported}) and job adverts.
For my time-line analysis I needed OpenBSD usage information between 1998 and 2005.
The best source of information I found, by far, of Open source OS usage derived from server logs (around 138 million Open source specific entries) is that provided by Distrowatch who count over 700 different distributions as far back as 2002. What is more Ladislav Bodnar the founder and executive editor of DistroWatch was happy to run a script I sent him to extract the count data I was interested in (I am not duplicating Distrowatch’s popularity lists here, just providing the 14 day totals for OS count data). Some analysis of this data below.
As luck would have it I recently read a paper by Diomidis Spinellis which had used server log data to estimate the adoption of Open Source within organizations. Diomidis researches Open source and was willing to run a script I wrote to extract the User Agent string from the 278 million records he had (unfortunately I cannot make this public because it might contain personal information such as email addresses, just the monthly totals for OS count data, tar file of all the scripts I used to process this raw log data; the script to try on your own logs is countos.sh).
My attempt to extract OS names from the list of User Agent strings Diomidis sent me (67% of of the original log entries did contain a User Agent string) provides some insight into the reliability of this approach to counting usage (getos.awk is the script to try on the strings extracted with the earlier script). There is no generally agreed standard for:
- what information should be present; 6% of UA strings contained no OS name that I knew (this excludes those entries that were obviously robots/crawlers/spiders/etc),
- the character string used to specify a given OS or a distribution; the only option is to match a known list of names (OS names used by Distrowatch,
missos.awkis the tar file script to print out any string not containing a specified list of OS names, the Wikipedia List of operating systems article), - quality assurance; some people cannot spell ‘windows’ correctly and even though the source is now available I don’t think anybody uses CP/M to access the web (at least 91 strings,
%, would not have passed).
Ladislav Bodnar thinks that log entries from the same IP addresses should only be counted once per day per OS name. I agree that this approach is much better than ignoring address information; why should a person who makes 10 accesses be counted 10 times, a person who makes one access is only counted once. It is possible that two or more separate machines running the same OS are accessing the Internet through a common gateway that results in them having the IP address from an external server’s point of view; this possibility means that the Distrowatch data undercounts the unique accesses (not a serious problem if most visitors have direct Internet access rather than through a corporate network).
The Distrowatch data includes counts for all IP address and from 13 May 2004 onwards unique IP address per day per OS. The mean ratio between these two values, summed over all OS counts within 14 day periods, is 1.9 (standard deviation 0.08) and the Pearson correlation coefficient between them is 0.987 (95% confidence interval is 0.984 to 0.990), i.e., almost perfect correlation.
The Spinellis data ignores IP address information (I got this dataset first, and have already spent too much time collecting to do more data extraction) and has 10 million UA strings containing Open source OS names (6% of all OS names matched).
How representative are the Distrowatch and Spinellis data? The data is as representative of the general OS population as the visitors recorded in the respective server logs are representative of OS usage. The plot below shows the percentage of visitors to Distrowatch that use Ubuntu, Suse, Redhat. Why does Redhat, a very large company in the Open source world, have such a low percentage compared to Ubuntu? I imagine because Redhat customers get their updates from Redhat and don’t see a need to visit sites such as Distrowatch; a similar argument can be applied to Suse. Perhaps the Distrowatch data underestimates those distributions that have well known websites and users who have no interest in other distributions. I have not done much analysis of the Spinellis data.
Presumably the spikes in usage occur around releases of new versions, I have not checked.
For my analysis I am interested in relative change over time, which means that representativeness and not knowing the absolute number of OSs in use is not a problem. Researchers interested in a representative sample or estimating the total number of OSs in use are going to need a wider selection of data; they might be interested in the following OS usage information I managed to find (yes I know about Netcraft, they charge money for detailed data and I have not checked what the Wayback Machine has on file):
- Wikimedia has OS count information back to 2009. Going forward this is a source of log data to rival Distrowatch’s, but the author of the scripts probably ought to update the list of OS names matched against,
- w3schools has good summary data for many months going back to 2003,
- statcounter has good summary data (daily, weekly, monthly) going back to 2008,
- TheCounter.com had data from 2000 to 2009 (csv file containing counts obtained from Wayback Machine).
If any reader has or knows anybody who has detailed OS usage data please consider sharing it with everybody.
My no loops in R hair shirt
Being professionally involved with analyzing source code, I get to work with a much larger number of programming languages than most people. There is a huge difference between knowing the intricate details of the semantics of a language and being able to fluently program in a language like a native developer. There are languages whose semantics I probably know better than nearly all its users and yet can only code in like a novice, and there are languages whose reference manual I might have read once and yet can write fluently.
I try not to learn new languages in which to write programs, they just clutter my brain. It can be very embarrassing having somebody sitting next to me while I write an example and not be able to remember whether the language I am using requires a then in its if-statement or getting the details of a print statement wrong; I am supposed to be a computer language expert.
Having decided to migrate from being a casual R user to being a native user (my current status is somebody who owns more than 10 books that make extensive use of R) I resolved to invest the extra time needed to learn how to write code the ‘R-way’ (eighteen months later I’m not sure that there is an ‘R way’ in the sense that could be said to exist in other languages, or if there is it is rather diffuse). One of my self-appointed R-way rules is that any operation involving every element of a vector should be performed using whole vector operations (i.e., no looping constructs).
Today I was analyzing the release history of the Linux kernel and wanted to get the list of release dates for the current version of the major branch; I had a list of dates for every release. The problem is that when a major release branch is started previous branches, now in support only mode, may continue to be maintained for some time, for instance after the version 2.3 branch was created the version 2.2 branch continued to have releases made for it for another five years.
The obvious solution to removing non-applicable versions from the release list is to sort on release date and then loop through the elements, removing those whose version number was less than the version appearing before them in the list. In the following excerpt the release of 2.3.0 causes the following 2.2.9 release to be removed from the list, also versions 2.0.37 and 2.2.10 should be removed.
Version Release_date 2.2.8 1999-05-11 2.3.0 1999-05-11 2.2.9 1999-05-13 2.3.1 1999-05-14 2.3.2 1999-05-15 2.3.3 1999-05-17 2.3.4 1999-06-01 2.3.5 1999-06-02 2.3.6 1999-06-10 2.0.37 1999-06-14 2.2.10 1999-06-14 2.3.7 1999-06-21 2.3.8 1999-06-22 |
While this is an easy problem to solve using a loop, what is the R-way of solving it (use the xyz package would be the answer half said in jest)? My R-way rule did not allow loops, so a-head scratching I did go. On the assumption that the current branch version dates would be intermingled with releases of previous branches I decided to use simple pair-wise comparison (which could be coded up as a whole vector operation); if an element contained a version number that was less than the element before it, then it was removed.
Here is the code (treat step parameter was introduced later as part of the second phase tidy up; data here):
ld=read.csv("/usr1/rbook/examples/regression/Linux-days.csv") ld$Release_date=as.POSIXct(ld$Release_date, format="%d-%b-%Y") ld.ordered=ld[order(ld$Release_date), ] strip.support.v=function(version.date, step) { # Strip off the least significant value of the Version id v = substr(version.date$Version, 1, 3) # Build a vector of TRUE/FALSE indicating ordering of element pairs q = c(rep(TRUE, step), v[1:(length(v)-step)] <= v[(1+step):length(v)]) # Only return TRUE entries return (version.date[q, ]) } h1=strip.support.v(ld.ordered, 1) |
This pair-wise approach only partially handles the following sequence (2.2.10 is greater than 2.0.37 and so would not be removed).
2.3.6 1999-06-10 2.0.37 1999-06-14 2.2.10 1999-06-14 2.3.7 1999-06-21 |
The no loops rule prevented me iterating over calls to strip.support.v until there were no more changes.
Would a native R speaker assume there would not be many extraneous Version/Release_date pairs and be willing to regard their presence as a minor data pollution problem? If so, I have some way to go before I might be able to behave as a native.
My next line of reasoning was that any contiguous sequence of non-applicable version numbers would probably be a remote island in a sea of applicable values. Instead of comparing an element against its immediate predecessor, it should be compared against an element step back (I chose a value of 5).
h2=strip.support.v(h1, 5) |
The original vector contained 832 rows, which was reduced to 745 and then down to 734 on the second step.
Are there any non-loop solutions that are capable of handling a higher density of non-applicable values? Do tell if you can think of one.
Update (a couple of days later)
Thanks to Charles Lowe, Wojtek and kaz_yos for their solutions using cummax, a function that I was previously unaware of. This was a useful reminder that what other languages do in the syntax/semantics R surprisingly often does via a function call (I’m still getting my head around the fact that a switch-statement is implemented via a function in R); as a wannabe native R speaker I need to remove my overly blinked language approach to problems and learn a lot more about the functions that come as part of the base system
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