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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.
Only compiler vendor customers, not its users, count
The hardest thing about working on compilers is getting somebody to pay you to do it (its a close run race against having the cpu instructions chop and change under you during initial development, but that’s another story). The major shift of compiler vendor business model from proprietary to open source has significant implications for users of compilers. Note I said user not customer, only one of them pays money. Under the commercial model there was usually a very direct connection between compiler user and customer (even in large organizations users rather than the manager who makes the purchase decision are often regarded by vendors as the customer), while under the Open source model most users are not customers (paying money for a distribution does not make you a customer of the people maintaining the compiler who probably don’t receive any of the money you spent).
Like all good businesses compiler vendors don’t want to make their customers unhappy. There is one way guaranteed to make all customers so unhappy that they will remember the experience for years; ship a new compiler release that breaks their existing code (this usually happens because their is a previously undetected bug in the code or because use is being made of an implemented defined/undefined part of the language {the compiler gets to decide what to do when it encounters such code}). Not breaking existing customer code is priority ONE in any commercial compiler development group.
Proprietary vendors have so many customers its almost impossible for them to know in advance what changes will break existing code and the only option is to be ultra conservative about adding new code optimizations (new optimizations can so easily change how source containing undefined behavior is processed). Ultra conservative is the polite term, management paranoia would be more accurate. There is another advantage to vendors for not breaking their customers’ code, they are protected against competition by new market entrants; a new vendor with a shiny go faster compiler doing all the optimizations the existing vendor was not willing to do in case it broke existing code will quickly find out that the performance improvements they offer are rarely big enough to tempt potential customers to switch compilers. Really, the only time companies switch compiler is when they have to port to a new platform to make a sale or their existing vendor goes bust.
Open source vendors (e.g., those commercially involved in support/maintenance of gcc or llvm) have relatively few customers (e.g., big companies paying them lots of money for specific reasons) and as always these customers want existing code to continue to work. If the customer is paying for a code generator for a previously unsupported processor then there probably isn’t any existing code for that processor; it is a fact of life that porting source to a new processor always involves work. Some Linux distributors (e.g., Suse and Redhat) are customers in the sense that they pay the salary of developers who spend a lot of their time in compiler maintenance/upgrades and presumably work to try and ensure that the code in their respective Linux distributions does not get broken.
Compiler users who are not customers don’t count on the code breakage front (well, count for very very little, if an update broke lots of different developers’ code and enough fuss was made there might well be an update than unbroke the previous one).
What can a user do if code that used to work ok is broken when compiled with a later version of the compiler? The obvious answer is to continue using the older version that produces the desired behavior, fixing the code causing the problem is a better answer (but might involve a lot of work). There is no point in flaming the compiler developers, you are not contributing towards their upkeep; Open source does not give users the consideration that a customer enjoys.
US DoD software development data now available
I found a huge resource of software development data last weekend at the Defense Cost and Resource Center (DCARC). The Software Resource Data Report part of this resource contains information on around 2,000 major software development projects (any US DOD project over $20 million+) giving details of schedule, developer experience, money spent per year, lines of code, amount of code change, hours spent on at various stages of development and a whole lot more.
The catch? The raw data is only available to DoD analysts 🙁 I was a bit surprised that laws got passed mandating the collection of this kind of information and a lot less surprised that the DoD don’t want to make detailed development information for missile systems, radar installations, etc available to some interested parties; those of us who are not going to go out and build such systems are collateral damage.
What is the US government’s reason for requiring the collection and dissemination of this information? They want to reduce the huge amount of money currently being spent on the software development component of military systems (often a very large slice of the total project costs). Will having this data available reduce costs? It will certainly get project managers a lot more worried about project cost/time overruns if they know that lots of people outside the project are going to see their ‘failure’.
Hopefully there are Open data activists in the US who will push for a redacted form of the software data being made available to all interested parties, rather like that provided by the USA Spending site. In the meantime there are a few lucky DoD analysts who have gone from famine to feast and are probably having trouble figuring out where to start.
Update
The military like to rename things, and move stuff around. We now have: Cost Assessment Data Enterprise, with its software data.
Programs spent a lot of time repeating themselves
Inexperienced software developers are always surprised that programs used by lots of people can contain many apparently non-trivial faults and yet continue to operate satisfactorily; experienced developers become familiar with this state of affairs and tend to shrug their shoulders. I have previously written about how software is remarkably fault tolerant. I think this fault tolerance is telling us something important about the characteristics of software and while I have some ideas about what it might be I don’t yet have a good handle (or data) on what is going on to lay out my argument.
In this article I’m going to talk about another characteristic of program execution which I think is connected to program fault tolerance and is also very surprising.
Software differs from hardware in that for a given set of inputs a program will always produce the same output, it will not wear out like hardware and eventually do something different (to simplify things I’m ignoring the possible consequences of uninitialized variables and treating any timing dependencies as part of the input set). So for a fault to be observed different input is required (assuming one exists and none appeared for the first input set).
I used to assume that during a program’s execution the basic cpu operations (e.g., binary arithmetic and bitwise operations) processed a huge number of different combinations of input values (e.g., there are 
combinations of input value for a 16-bit add operation) and was very surprised to find out this is not the case. For many programs around 80% of all executed instructions are repeat instructions, that is a given instruction, such as add, operates on the same combinations of input values that it has previously operated on (while executing the program) to generate an output value that is identical to the one previously generated from these input values. If we count the number of static instructions in the program (i.e., the number of assembly instructions in a listing of the disassembled executable program) then 20% of them account for 90% of the repeated instructions; so a small amount of code (i.e., 20%) is not only responsible for most dynamically executed instructions but around 72% (i.e., 80%*90%) of these instructions repeat previous computations. If a large percentage of a what goes on internally within a program is repetition is it any surprise that once it works for a reasonable set of inputs it will probably work on other inputs?
Hang on you say, perhaps the percentage of repeat instructions is very high for a given set of external input values (e.g., a file to compress, compile or display as a jpeg) but there is a lot of variation in the set of repeat instructions between different external inputs. Measurements suggest this is not the case, with around 20% of dynamic instructions having input values that can be traced to external program input (12-30% come from globally initialized variables and the rest are generated internally).
There is a technical detail that reduces the repeat instruction percentages given about by a factor of two; researchers always like to give the most favorable numbers and for this discussion we need to make a distinction between local repetition which counts one instruction and its inputs/outputs at a particular point in the code and global repetition which counts all instructions of a given kind irrespective of where they occur in the code. A discussion of fault behavior needs to look at local repetition, not global repetition; there is a factor of two difference in the dynamic percentage and some reduction in the percentage of static instructions involved.
Sometimes the term redundant computation is used, as if the cpu should remember what happened last time it executed an instruction with a particular set of inputs and reuse the answer it got last time. Researchers have proposed caching the results of executing an instruction with a given set of input values and speeding things up or saving power by reusing previous results rather than recalculating them (a possible speedup of 13% on SPEC95 is claimed for a reuse buffer containing 4096 entries).
So a small percentage of the instructions in a program account for most of the execution time (a generally known characteristic) and around 30% of the executed instructions operate on input values they have processed before to produce output they have produced before (to the extent that a cache containing a few thousand entries is big enough to hold the a large percentage of the duplicates). If encountering a new fault requires different execution behavior to occur then having a large percentage of a program always doing the same thing (i.e., same input values same output value) will have a significant impact on the likelihood of encountering a fault. Part of the reason programs are fault tolerant is because external input values don’t have a big an impact on program behavior as we might have thought.
Researchers have also investigated repeats involving units larger than one instruction, such as sub-blocks (a sequence of instructions smaller than a basic block) and even complete functions or just the mathematical ones.
The raw data is obtained using cpu simulators to monitor programs as they are executed, logging the values read as input by an instruction and the value generated as output (in most cases the values are read from registers and written to a register). A single study might log billions of instructions from the SPEC benchmark.
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