The purpose of software engineering research is to figure out how software development works so that the software industry can improve its quality/timeliness (i.e., lower costs and improved customer satisfaction). Research is hampered by the fact that companies are not usually willing to make public good quality data about the details of their software development processes.
In mid July a post on the ACCU general mailing list caught my eye and I followed a link to a very interesting report, went to visit 7digital a few weeks later, told them about my empirical software engineering with R book and how I wanted to make all the data I used available to readers and they agreed to make the data public! The data arrived at the start of August and I spent the rest of the month analyzing it (the R code I used to analyse it).
Below is a draft of what will eventually appear in the book. As always comments welcome, particularly if you can extract more information from the 7digital data (the mapping of material to WordPress blog format might still be flaky in places).
Traditionally software development projects work towards releasing product updates on prespecified dates, often with a release cycle of between once or twice a year and with many updates included in each release. In contrast to this approach development groups following an Agile method <book ???> make frequent releases with each containing a small incremental update (Agile is an umbrella term applied to a variety of iterative and incremental software development methodologies).
Rationale for the Agile approach includes getting rapid feedback from customers on the direction of developments and maximizing return on software investment by getting newly implemented features into customers hand almost immediately.
The large number of releases (compared to other approaches) has the potential to provide enough data for meaningful statistical analysis of questions such as how often new features are released and the number of features under development at any time.
7digital<book 7Digital_12> is a digital media delivery company that operates an international on-line digital music store (www.7digital.com) and provides business to business digital media services via an open API platform. At 7digital software development is done using an Agile process and since April 2009 various items of information have been recorded <book Bowley_12>; 7digital are open about there process and have made this information publicly available and it is analysed here.
The data consists of information on the 3,238 features implemented by the 7digital team between April 2009 and July 2012; this information consists of three dates (Prioritised/Start Development/Done), a classification of the feature as one of nine possible internal types (i.e.,
During the recording period the number of developers grew from 14 to 35.
The start/done dates represent an elapsed time period, a wide variety of factors can cause work on the implementation of a feature to be stalled for a period of time, i.e., the time difference need not represent total development time.
The Agile process gives a great deal of flexibility to developers about which projects they chose to work on. Information on the number of developers working on the implementation of individual features was not recorded.
As discussed elsewhere [checking data quality] measurements involving people are likely to be subject more external influences than measurements of inanimate objects such as source code, they are also more difficult to replicate and are open to those being measured influencing the results in their favor.
The following is what is known about the 7digital measurement process.
The data recording was done by whoever ran the Agile stand-up session at the start of the day.
What unit of time measurement is appropriate for analysing an Agile process? While fine grained measurements are the ideal they have the potential to require nontrivial effort from those reporting the values, are open to individual interpretation (e.g., when exactly did work start/stop on this feature?) and subject to human error (e.g., forgetting to note the event when it happened and having to recall it later). The day was chosen as the basic unit of time measurement; in light of the time needed to implement most features this may seem too large, but this choice has the advantage of being the natural unit of measurement in that developers meet together every morning to discuss progress and that days work and being so broad makes it more likely that start/end times will be consistently applied as well as less prone to inaccurate recall later.
Goodhart’s law (it is really an observation of human behavior rather than a law) says “Any observed statistical regularity will tend to collapse once pressure is placed on it for control purposes.” If the measurements collected were actively used to control or evaluate the development team then the developers would be motivated to move the measurements in the direction that was favorable to them. 7digital do not attempt to use the measurements for control or evaluation or developers and developers have no motive change their behavior based on being measured.
I find the data believable in that the measurement process is not so expensive or cumbersome that developers are unwilling to attempt to report accurate data and not being directly effected by the results means they have no motive for changing their behavior to influence the measurements.
Believable data does not mean the data is error free. The following is a count of the days of the week on which feature implementations were recorded as being Done. Monday is day 0 and the counts for Saturday/Sunday should be zero; assuming that Friday/Monday had been intended the non-zero values suggest a 2-4% error rate, comparable with human error rates for low stress/non-critical work.
> table(Done.day[(Done.day <= 650)] %% 7) 0 1 2 3 4 5 6 227 225 214 243 177 8 7 > table(Done.day[(Done.day > 650)] %% 7) 0 1 2 3 4 5 6 443 483 455 473 270 4 9
Your author is not aware of any empirically based theory of Agile feature development capable of making predictions about development time related questions.
The analysis described here is purely descriptive; there is no attempt to build predictive models or compare the data against any existing theory.
The results from this data analysis (and all analysis in this book) are to provide information that will help software developers do a better job. What information can be extracted that would be useful to 7digital? This has proved to be a something of a chicken-and-egg question because people are interested in seeing the results before deciding whether they are useful. The following issues are of general interest:
- characteristics of the time taken to implement new features,
- variations in the number of different kinds of features (e.g., bug/non-bug) over time,
Overview of data
The data consists of start/finish times for the implementations of features and the overview information that springs to mind is average number of features implementation starts per time interval and average time taken to implement a feature. The figure below is a good enough approximation to this information to get a rough idea of its characteristics (e.g., the effect of weekends and holidays have not been taken into account and a 30 day rolling mean has been applied to smooth out daily fluctuations).
Figure 1. Average number of feature implementations started (blue) and their average duration (red); a 30 day rolling mean has been applied to both. Data courtesy of 7digital.
The plot appears to have two parts, before and after day 650 (or thereabouts). After day 650 the oscillations in feature implementation time die down substantially and the rate at which new feature implementations are started steadily increases. Possible reasons for the larger variations in the first 650 days include less expertise in organizing features into smaller work items and larger features being needed during the earlier stages of product development.
Obviously shorter implementation times make it possible to start work on more new features, however new feature starts continues to increase while implementation time stabilises around a lower value. Possible causes for the continuing increase in new feature starts include an increase in the number of developers and/or existing developers becoming more skilled in breaking work down into smaller features (i.e., feature implementation time stays about the same because fewer developers are working on each feature, making developers available to start on new features).
Software product development is a complicated business and a wide variety of different events and processes are likely to have contributed to the patterns of behavior seen in the data. While developers write the software it is customers who report most of the bugs and one of the goals of following an Agile methodology is rapid response to customer feedback (e.g., deciding which features need to be implemented and which left out). Customer information is not present in the dataset.
Are the same processes generating the apparent two phase behavior?
Any pattern of behavior is generated by a set of processes and when a pattern of behavior changes it is worthwhile asking how the processes driving the behavior changed.
Fitting a statistical distribution to a dataset is useful in that many distributions are known to be generated by processes having specified behaviors. Being able to fit the same distribution to both the pre and post 650 day datasets suggests that the phase change seen was not a fundamental change but akin to turning the volume knob of the distribution parameters one way or the other. If the datasets are best fitted by different distributions then the processes generating the two patterns of behavior are potentially very different.
Of the two characteristics plotted the feature implementation time appears to undergo the largest change of behavior and so the distribution of implementation times for the two phases is analysed here.
|Moment||Initial 650 days||After 650 days|
A quick look at the data shows that many features are implemented in a single day and only a few take more than a week, one distribution having this pattern of behavior is the power-law. The table above shows that the variance is much larger than the mean and the distribution has a large positive skew, properties shared by the [negative binomial distribution]. The figure below is a plot of the number of features requiring a given number of elapsed working days for their implementation (top first 650 days, all features finished after 650 days), along with two power-law and a negative binomial distribution fit to the data.
Figure 2. Number of features whose implementation took a given number of elapsed workdays. Top first 650 days, bottom after 650 days. Green line is the fitted negative binomial distribution. Data courtesy of 7digital.
The power-law fits were obtained by splitting the data into two parts, shorter/longer than 16 days (after noticing that visually the combined dataset seemed to have this form, less noticeable in the two subsets) and performing nonlinear regression using
nls to find good fits for the parameters
b (whose initial starting values converged without needing manual tuning).
pow_equ=nls(num.features ~ a*days^b, start=list(a=1200, b =-2)) y=predict(pow_equ, days) lines(days, y)
While the power-law fits are not very good overall one of them does provide an easy to remember seat of the pants method for approximating the probability of a project taking a small number of days to complete (e.g., for it is
sum(1/(1:16)) is 3.38]). The approximation is also a reasonable fit for subsets of the features (e.g., different kinds of bugs).
The R package
fitdistrplus contains functions for matching and fitting a dataset against known commonly occurring distribution. The Cullen and Frey graph produced by a call to
descdist suggests that a negative binomial distribution is the best fitting of those tested (agreeing with the ad-hoc conclusion jumped to above).
descdist(p\$Cycle.Time, discrete=TRUE, boot=100)
fitdist returns values for the parameters providing the appropriate fit to the specified dataset and distribution.
fd=fitdist(p\$Cycle.Time, "nbinom", method="mle") # Fit to a negative binomial distribution size.ct=fd\$estimate mu.ct=fd\$estimate # Plot distribution using fitted parameters plot(dnbinom(1:93, size=size.ct, mu=mu.ct)*length(p\$Cycle.Time), xlim=c(1,90), ylim=c(1,1200), log="xy")
The figure above shows that the negative binomial distribution could be a reasonable fit if the percentage of single day features was not so high. Two possibilities spring to mind:
- the data does not include any counts for zero days which is one of the possible values supported by the negative binomial distribution (obviously feature implementations cannot take zero days),
- measurement quantization introduces significant uncertainty for shorter implementations, if the minimum unit of measurement were less than 1 day the fit might be much better because some feature implementations take half-a-day while others take a whole day.
It is possible to adjust the negative binomial equation to move the lower bound from zero to one. The package
gamlss supports what is known as zero-truncation and the figure below shows the zero-truncated negative binomial distribution fitted to the pre/post 650 day counts.
Figure 3. A zero-truncated negative binomial distribution fitted to the number of features whose implementation took a given number of elapsed workdays; top first 650 days, bottom after 650 days. Data courtesy of 7digital.
The quality of fit is much better for the pre 650 day data compared to the post 650 data.
> qual.pre650 AIC log.likelihood 6109.225 -3052.612 > qual.post650 AIC log.likelihood 9923.509 -4959.754
Modifying the negative binomial distribution to handle a dataset not containing zeroes improves the fit, can the fit be further improved by adjusting for measurement quantization?
One possibility is to simulate measuring feature implementation in units smaller than a day; the following code multiplies the implementation time by two and randomly decides whether to subtract one, i.e., maps measurements made in days to a possible set of measurements made in half days.
num.features=length(cycle.time) dither=as.integer(runif(num.features, 0, 1) > 0.33) return(2*cycle.time-dither)
Fitting 1,000 randomly modified half-day measurements and averaging over all results shows that the fit is slightly worse than the original data (as measured by various goodness of fit criteria):
> fit.quality(p\$Cycle.Time[Done.day < 650]) loglikelihood AIC BIC -3438.284 6880.567 6890.575 > rowMeans(replicate(1000, fit.quality(sub.divide(p\$Cycle.Time[Done.day < 650])))) loglikelihood AIC BIC -4072.721 8149.442 8159.450
As discussed in the section on [properties of distributions] the negative binomial distribution can be generated by a mixture of [Poisson distribution]s whose means have a [Gamma distribution]. There are other distributions that can be generated through a mixture of Poisson distributions, are any of them a better fit of the data? The Delaporte distribution <book ???> sometimes fits very slightly better and sometimes slightly worse (see chapter source code for details); the difference is not large enough to warrant switching from a relatively well known distribution to one that is rarely covered in text books or supported in software; if data from other projects is best fitted by a Delaporte distribution then a switch may well be worthwhile.
The data subset corresponding to
p$Type == "Production Bug" fits significantly better than the complete dataset (i.e., AIC = 3729) while the fit for the subset
p$Type == "MMF" is comparable to the complete dataset (i.e., AIC of 7251).
Both datsets appear to follow the same distribution, the negative binomial distribution (with zero-truncation), with the initial 650 days having a greater mean and variance than post 650 days. The Poisson distribution is often encountered in processes involving events in time and one can imagine it applying to the various processes involved in the implementation of a feature; why the means of these Poisson distributions might follow a Gamma distribution is harder to fathom and is left for another day (it implies that both the Poisson means are decreasing and that the variance of the means is decreasing)
Do any other equations fit the data? Given enough optional parameters it is always possible to find an equation that is a good fit to the data. The following call to
nls shows that the equation fits the complete dataset rather well.
exp_mod=nls(num.features ~ a*exp(b*days^c), start=list(a=10000, b=-2.0, c=0.4))
This equation is unappealing because of its lack of similarity with equations seen in many other areas of research, an exponential whose exponent has the form of raised to a fractional power is rarely encountered. There is a great deal of uncertainty when analysing data for the first time and being able to fit a form of equation used by other researchers provides a big comfort factor.
How many new feature implementations are started on each day?
The table below give the probability of a given number of new feature implementations starting on any day. There are sufficient multi-day implementations that on almost 20% of days no new feature implementations are started. An exponential equation is the commonly encountered form that provides an approximate fit to these values (i.e., ).
Time dependent patterns in the data
7digital is a growing company and we would expect that the rate of creation of features would increase over time, also as the size of the code base and the customer base increases the rate at which bugs are accepted for fixing is likely to increase.
The number of features developments started per day is one way of comparing different types of features. Plotting this information (see top left) shows that there is a great deal of variation over very short periods of time. This variation can be smoothed using a [rolling mean] to bring out the trends (the
rollmean function in package
zoo); the other plots show 20, 50 and 120 day rolling means for bugs (red) and non-bugs (blue) and the non-bug/bug feature ratio (black).
Figure 4. Number of feature developments started on a given work day (red bug fixes, blue non-bug work, black ratio of two values; 20 day rolling mean bottom left, 50 day top right, 120 day bottom right).
Both the number of bugs and non-bug features has trended upwards, as has the ratio between them. While it is tempting to suggest that this increase has been generated by the significant increase in number of developers over the time period, it is also possible the group has become better at dividing work into smaller feature work items or that having implemented the basic core of the products less work is now needed to create new features. The information present in the data is not sufficient to attempt to provide believable explanations for the upward trend.
Time series analysis
A preliminary data analysis technique for time data is to plot the current values against their lagged values for various lags. The output from the R function
lag.plot for the number of in-progress features is shown below; apart from clustering the plots do not show any noticeable relationships in the data.
Figure 5. Scatterplot of number of features currently in-progress against various time lags (in working days).
Over longer timescales do the number of in-progress feature implementations have noticeable seasonal variations (e.g., greater in summer and Christmas/year year when developers are likely to be away)?
[Autocorrelation] is the cross-correlation of a time varying signal with itself, i.e., the correlation between a measurement occurring at time and another one occurring at time ; changes in correlation as increases can be used to infer information about periodic changes over time.
The number of in-progress features appears to be increasing over time (top left of figure below) and this trend away from zero needs to be adjusted for before an autocorrelation is calculated. The feature implementation recording process did not happen over night and took a while before it covered all work performed; comparing a linear fit of all data (pink line of top left of figure below) and all data from January 2010 (red line) shows that this startup period does not significantly bias the growth trend. However, it is possible that patterns of behavior present in the total set of work items over a period are not reflected in the first 250 days of recording (roughly 180 working days) and so these are excluded from this particular analysis. From feature duration measurements we know that over 70% of features take longer than a day to implement, so the data contains a lot of serial dependence which may affect the accuracy of the results.
trend=lm(day.totals ~ time(day.totals)) plot(day.totals, xlab="Days since Apr 2009", ylab="Features in-progress") abline(trend) day.detrend=day.totals - predict(trend) # Subtract out any global trend
The bottom left of the figure below shows the variation of in-progress features about the trend line. The top right shows the autocorrelation function for this plot, the regular spikes are caused by weekends (when no work took place). Removing weekends from the analysis results in the autocorrelation shown in the bottom right.
Apart from some correlation having a one day lag the autocorrelation drops to zero almost immediately followed by what appear to be small random spikes. These small spikes do not look important enough to follow up. A very similar pattern is seen in the autocorrelation of the two 650-day phases (the initial 650 days has a larger correlation for lags of 2-5 days). It is possible that a seasonal oscillation in feature work exists but is not seen because the data is so noisy (i.e., contains significant variation between adjacent days).
Summing daily values to create weekly totals, which of provides some smoothing, and performing the above analysis again produces essentially the same results.
Figure 6. The number of features currently in-production on a given day since April 2009 (top left, pink line is a linear fit of all data, red line a linear fit of the data after day 250), the variation in this number about a linear trend line, excluding the first 250 days (bottom left), the autocorrelation function (top right) and the autocorrelation function with weekends removed from the data (bottom right).
Do reported bugs correlate with new feature releases?
When a feature is released the probability of a new bug being reported increases. Whether different bug probabilities should be assigned to bugfix releases and non-bugfix releases is discussed below. Based on this expectation we would expect to see a [cross correlation] between releases and number of bugs accepted for fixing. The more code a feature contains the more likely it is to contain a bug; however, no information on feature code size is provided so number of implementation work days is used as a measure of feature size.
The data does not specify which bugs belong to which features. It is to be expected that over time the probability of a bug being reported against a feature will decrease, reasons for this behavior include bugfixing, customers no longer using a feature and features being superseded by newer ones.
The figure below is the cross correlation between the ‘size’ of all features recorded as Done on a given day and all bugs recorded as Prioritised on a given date; the top plot is for all non-bugfix feature releases while the bottom plot is for all feature releases.
Figure 7. Cross correlation of feature release ‘size’ (top non-bugfix releases, bottom all releases) and date when bugs are prioritised.
The feature/bug cross correlation in the figure above should be zero for negative lags (i.e., no bugs can be reported for features that have not yet been released). One way of interpreting the pattern of correlation is that there some bugs are reported immediately after the release (perhaps by early adopters) followed by more bugs some 20 to 50 working days after release; other interpretations include there being a small amount of signal just visible behind lots of noise in the data or that the approximation used to estimate feature size is too crude.
Using weekly totals produces essentially the same result.
The distribution of feature implementation times appears to follow a negative binomial distribution (with zero-truncation), with the values for the initial 650 days having a greater mean and variability (i.e., variance) than the following days.
There appears to be too much noise in the data for any time series signal involving mean values or a relationship between releases and bugs to be reliably extracted.
Thanks to 7digital for making the data available and being willing to make it public and to Rob Bowley for helping me to understand 7digital’s development environment.