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Posts Tagged ‘R’

Ecosystems chapter added to “Empirical software engineering using R”

July 17th, 2017 No comments

The Ecosystems chapter of my Empirical software engineering book has been added to the draft pdf (download here).

I don’t seem to be able to get away from rewriting everything, despite working on the software engineering material for many years. Fortunately the sparsity of the data keeps me in check, but I keep finding new and interesting data (not a lot, but enough to slow me down).

There is still a lot of work to be done on the ecosystems chapter, not least integrating all the data I have been promised. The basic threads are there, they just need filling out (assuming the promised data sets arrive).

I did not get any time to integrate in the developer and economics data received since those draft chapters were released; there has been some minor reorganization.

As always, if you know of any interesting software engineering data, please tell me.

I’m looking to rerun the workshop on analyzing software engineering data. If anybody has a venue in central London, that holds 30 or so people+projector, and is willing to make it available at no charge for a series of free workshops over several Saturdays, please get in touch.

Projects chapter next.

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Warp your data to make it visually appealing

May 4th, 2017 No comments

Data plots can sometimes look very dull and need to be jazzed up a bit. Now, nobody’s suggesting that the important statistical properties of the data be changed, but wouldn’t it be useful if the points could be moved around a bit, to create something visually appealing without losing the desired statistical properties?

Readers have to agree that the plot below looks like fun. Don’t you wish your data could be made to look like this?

Rabbit image

Well, now you can (code here, inspired by Matejka and Fitzmaurice who have not released their code yet). It is also possible to thin-out the points, while maintaining the visual form of the original image.

The idea is to perturb the x/y position of very point by a small amount, such that the desired statistical properties are maintained to some level of accuracy:

check_prop=function(new_pts, is_x)
{
if (is_x)
   return(abs(myx_mean-stat_cond(new_pts)) < 0.01)
else
   return(abs(myy_mean-stat_cond(new_pts)) < 0.01)
}
 
 
mv_pts=function(pts)
{
repeat
   {
   new_x=pts$x+runif(num_pts, -0.01, 0.01)
   if (check_prop(new_x, TRUE))
      break()
   }
 
repeat
   {
   new_y=pts$y+runif(num_pts, -0.01, 0.01)
   if (check_prop(new_y, FALSE))
      break()
   }
 
return(data.frame(x=new_x, y=new_y))
}

The distance between the perturbed points and the positions of the target points then needs to be calculated. For each perturbed point its nearest neighbor in the target needs to be found and the distance calculated. This can be done in n log n using kd-trees and of course there is an R package, RANN, do to this (implemented in the nn2 function). The following code tries to minimize the sum of the distances, another approach is to minimize the mean distance:

mv_closer=function(pts)
{
repeat
   {
   new_pts=mv_pts(pts)
   new_dist=nn2(rabbit, new_pts, k=1)
   if (sum(new_dist$nn.dists) < cur_dist)
      {
      cur_dist <<- sum(new_dist$nn.dists)
      return(new_pts)
      }
   }
 
}

Now it’s just a matter of iterating lots of times, existing if the distance falls below some limit:

iter_closer=function(tgt_pts, src_pts)
{
cur_dist <<- sum(nn2(tgt_pts, src_pts, k=1)$nn.dists)
cur_pts=src_pts
for (i in 1:5000)
   {
   new_pts=mv_closer(cur_pts)
   cur_pts=new_pts
   if (cur_dist < 13)
      return(cur_pts)
   }
return(cur_pts)
}

This code handles a single statistical property. Matejka and Fitzmaurice spent more than an hour on their implementation, handle multiple properties and use simulated annealing to prevent being trapped in local minima.

An example, with original points in yellow:

Warp towards rabbit image

Enjoy.

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Economics chapter added to “Empirical software engineering using R”

March 26th, 2017 No comments

The Economics chapter of my Empirical software engineering book has been added to the draft pdf (download here).

This is a slim chapter, it might grow a bit, but I suspect not by a huge amount. Reasons include lots of interesting data being confidential and me not having spent a lot of time on this topic over the years (so my stash of accumulated data is tiny). Also, a significant chunk of the economics data I have is used to discuss issues in the Ecosystems and Projects chapters, perhaps some of this material will migrate back once these chapters are finalized.

You might argue that Economics is more important than Human cognitive characteristics and should have appeared before it (in chapter order). I would argue that hedonism by those involved in producing software is the important factor that pushes (financial) economics into second place (still waiting for data to argue my case in print).

Some of the cognitive characteristics data I have been waiting for arrived, and has been added to this chapter (some still to be added).

As always, if you know of any interesting software engineering data, please tell me.

I am after a front cover. A woodcut of alchemists concocting a potion appeals, perhaps with various software references discretely included, or astronomy related (the obvious candidate has already been used). The related modern stuff I have seen does not appeal. Suggestions welcome.

Ecosystems next.

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Empirical Software Engineering using R: first draft available for download

January 29th, 2017 4 comments

A draft of my book Empirical Software Engineering using R is now available for download.

The book essentially comes in two parts:

  • statistical techniques that are useful for analyzing software engineering data. This draft release contains most of the techniques I plan to cover. I am interested in hearing about any techniques you think ought to be covered, but I only cover techniques when real data is available to use in an example,
  • six chapters covering what I consider to be the primary aspects of software engineering. This draft release includes the Human Cognitive Characteristics chapter and I am hoping to release one each of the remaining chapters every few months (Economics is next).

There is a page for making suggestions and problem reports.

All the code+data is available and I am claiming to have a copy of all the important, publicly available, software engineering data. If you know of any I don’t have, please let me know.

I am looking for a publisher. The only publisher I have had serious discussions with decided not to go ahead because of my insistence of releasing a free copy of the pdf. Self-publishing is a last resort.

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Automatically generated join-the-dots images

December 16th, 2016 1 comment

It is interesting to try and figure out what picture emerges from a join-the-dots puzzle (connect-the-dots in some parts of the world). Let’s have a go at some lightweight automatic generation such a puzzle (some heavy-weight techniques).

If an image is available, expressed as an boolean matrix, R’s sample function can be used to select a small percentage of the black points.

Taking the output of the following equation:

x=seq(-4.7, 4.7, by=0.002)
 
y1 = c(1,-.7,.5)*sqrt(c(1.3, 2,.3)^2 - x^2) - c(.6,1.5,1.75)  # 3
y2 =0.6*sqrt(4 - x^2)-1.5/as.numeric(1.3 <= abs(x))           # 1
y3 = c(1,-1,1,-1,-1)*sqrt(c(.4,.4,.1,.1,.8)^2 -(abs(x)-c(.5,.5,.4,.4,.3))^2) - c(.6,.6,.6,.6,1.5) # 5
y4 =(c(.5,.5,1,.75)*tan(pi/c(4, 5, 4, 5)*(abs(x)-c(1.2,3,1.2,3)))+c(-.1,3.05, 0, 2.6))/
	as.numeric(c(1.2,.8,1.2,1) <= abs(x) & abs(x) <= c(3,3, 2.7, 2.7))                 # 4
y5 =(1.5*sqrt(x^2 +.04) + x^2 - 2.4) / as.numeric(abs(x) <= .3)                            # 1
y6 = (2*abs(abs(x)-.1) + 2*abs(abs(x)-.3)-3.1)/as.numeric(abs(x) <= .4)                    # 1
y7 =(-.3*(abs(x)-c(1.6,1,.4))^2 -c(1.6,1.9, 2.1))/
	as.numeric(c(.9,.7,.6) <= abs(x) & abs(x) <= c(2.6, 2.3, 2))                       # 3

and sampling 300 of the 20,012 points we get images such as the following:

Sampled rabbit image

A relatively large sample size is needed to reduce the possibility that a random selection fails to return any points within a significant area, but we do end up with many points clustered here and there.

library("plyr")
 
rab_points=adply(x, 1, function(X) data.frame(x=rep(X, 18), y=c(
	c(1, -0.7, 0.5)*sqrt(c(1.3, 2, 0.3)^2-X^2) - c(0.6, 1.5 ,1.75),
	0.6*sqrt(4 - X^2)-1.5/as.numeric(1.3 <= abs(X)),
	c(1, -1, 1, -1, -1)*sqrt(c(0.4, 0.4, 0.1, 0.1, 0.8)^2-(abs(X)-c(0.5, 0.5, 0.4, 0.4, 0.3))^2) - c(0.6, 0.6, 0.6, 0.6, 1.5),
	(c(0.5, 0.5, 1, 0.75)*tan(pi/c(4, 5, 4, 5)*(abs(X)-c(1.2, 3, 1.2, 3)))+c(-0.1, 3.05, 0, 2.6))/
		as.numeric(c(1.2, 0.8, 1.2, 1) <= abs(X) & abs(X) <= c(3,3, 2.7, 2.7)),
	(1.5*sqrt(X^2+0.04) + X^2 - 2.4) / as.numeric(abs(X) <= 0.3),
	(2*abs(abs(X)-0.1)+2*abs(abs(X)-0.3)-3.1)/as.numeric(abs(X) <= 0.4),
	(-0.3*(abs(X)-c(1.6, 1, 0.4))^2-c(1.6, 1.9, 2.1))/
		as.numeric(c(0.9, 0.7, 0.6) <= abs(X) & abs(X) <= c(2.6, 2.3, 2))
						)))
rab_points$X1=NULL
rb=subset(rab_points, (!is.na(x)) & (!is.na(y) & is.finite(y)))
 
x=sample.int(nrow(rb), 300)
plot(rb$x[x], rb$y[x],
	bty="n", xaxt="n", yaxt="n", pch=4, cex=0.5, xlab="", ylab="")

A more uniform image can produced by removing all points less than a given distance from some selected set of points. In this case the point in the first element is chosen, everything close to it removed and the the processed repeated with the second element (still remaining) and so on.

rm_nearest=function(jp)
{
keep=((dot_im$x[(jp+1):(jp+window_size)]-dot_im$x[jp])^2+
      (dot_im$y[(jp+1):(jp+window_size)]-dot_im$y[jp])^2) < min_dist
keep=c(keep, TRUE) # make sure which has something to return
return(jp+which(keep))
}
 
window_size=500
cur_jp=1
dot_im=rb
 
while (cur_jp <= nrow(dot_im))
   {
#   min_dist=0.05+0.50*runif(window_size)
   min_dist=0.05+0.30*runif(1)
   dot_im=dot_im[-rm_nearest(cur_jp), ]
   cur_jp=cur_jp+1
   }
 
plot(dot_im$x, dot_im$y,
	bty="n", xaxt="n", yaxt="n", pch=4, cex=0.5, xlab="", ylab="")

Since R supports vector operations I want to do everything without using loops or if-statements. Yes, there is a while loop :-(, alternative, simple, non-loop suggestions welcome.

Removing points with an average squared distance less than 0.3 and 0.5 we get (with around 135-155 points) the images:

Remove closest points rabbit image

I was going to come up with a scheme for adding numbers, perhaps I will do this in another post.

Click for more equations generating images.

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Producing software for money and/or recognition

October 24th, 2016 No comments

In the commercial environment money makes the world go around, while in academia recognition (e.g., number of times your work is cited, being fawned over at conferences, impressive job titles) is the coin of the realm (there are a few odd balls who do it out of love for the subject or a desire to understand how things work, but modern academia is a large bureaucracy whose primary carrot is recognition).

There is an incentive problem for those writing software in academia; software does not attract much, if any, recognition.

Does the lack of recognition for writing software matter? Surely what counts are the research results, not the tools used to get there (be they writing software or doing mathematics).

A recent paper bemoans the lack of recognition for the development of Python packages for Astronomy researchers. Well, its too late now, they have written the software and everybody gets to make perfect copies for free.

What the authors of Astropy want, is for researchers who use this software to include a citation to it in any published papers. Do all 162 authors deserve equal credit? If a couple of people add a new package, should they get a separate citation? What if a new group of people take over maintenance, when should the citation switch over from the old authors/maintainers to the new ones? These are a couple of the thorny questions that need to be answered.

R is perhaps the most widely used academic developed software ecosystem. A small dedicated group of people has invested a lot of their time over many years to make something special. A lot more people have invested effort to create a wide variety of add-on packages.

The base R library includes the citation function, which returns the BibTeX information for a given package; ready to be added to a research papers work flow.

Both commercial and academic producers need to periodically create new versions to keep ahead of the competition, attract more customers and obtain income. While they both produce software to obtain ‘income’, commercial and academic software systems have different incentives when it comes to support for end users of the software.

Keeping existing customers happy is the way to get them to pay for upgrades and this means maintaining compatibility with what went before. Managers in commercial companies make sure that developers don’t break backwards compatibility (developers hate having to code around what went before and would love to throw it all away).

In the academic world it does not matter whether end users upgrade, as long as they cite the package, the version used is irrelevant; so there is a lot less pressure to keep backwards compatibility. Academics are supposed to create new stuff, they are researchers after all, so the incentives are pushing them to create brand new packages/systems to be seen as doing new stuff (and obtain a whole new round of citations). A good example is Hadley Wickham, who has created some great R packages, who seems to be continually moving on, e.g., reshape -> reshape2 -> tidyr (which is what any good academic is supposed to do).

The run-time performance of a system is something end users always complain about, but often get used to. The reason is invariably that there is little or no incentive to address this issue (for both commercial and academic systems). Microsoft Windows is slower than it need be and the R interpreter could go a lot faster (the design of the interpreter looks like something out of the 1980s; I’m seeing a lot of packages in R only, so the idea that R programs spend all their time executing in C/Fortran libraries may be out of date. Where is the incentive to use post-2000 designs?)

How many new versions of a software package can be produced before enough people stop being willing to pay for an update? How many different packages solving roughly the same problem can academics produce?

I don’t think producing new packages for income has a long term future.

Software engineering data sets

September 5th, 2016 No comments

The pretty pictures from my empirical software engineering book are now online, along with the 210 data sets and R code (330M).

Plotting the number of data sets in each year shows that empirical software engineering has really taken off in the last 10 years (code+data). Around dozen or so confidential data sets are not included; I am only writing about data that can be made public.

Number of data sets per year

It used to be rare to find the data associated with a paper on the author’s website. Of course, before around 1995 there was no web, but since around 2012 the idea has started to take off.

Contact via email goes back to 1985 and before that people sent mag tapes through the post and many years ago somebody sent me punched tape (there is nothing like seeing the bits with the naked eye).

I have sent several hundred emails asking for data and received 55 data sets. I’m hoping this release will spur those who have promised me data to invest some time to send it.

My experience is that research data often lives on laptops and dies when the laptop is replaced (a study of biologists, who have been collecting data for hundreds of years, found a data ‘death rate’ of 17% a year). Had I started actively collecting data before 2010 the red line in the plot would be much higher for earlier years; I often received data from authors when writing my C book at the start of the century (Google went from nothing to being the best place to search, while I wrote).

In nine cases I extracted the data, either from the pdf or an image and then reverse engineered values.

I have around 50 data sets waiting to be processed. Given that lots more are bound to arrive before the book is finished, I expect to easily reach the 300 mark. A tiny number given my aim of writing about all software engineering issues for which public data exists.

If you know of interesting software engineering data, that is not to be found in these plots, please let me know.

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cpu+FPGA: applications can soon have bespoke instructions

March 21st, 2016 2 comments

Compiler writers are always frustrated that the cpu they are currently targeting does not contain the one instruction that would enable them to generate really efficient code. If only it were possible to add new instructions to the cpu. Well, it looks like this will soon be possible; Intel have added an on chip FPGA to their Broadwell processor (available circa 2017).

Having custom instructions on a FPGA (they would be loaded at program startup) is not the same as having the instructions on the cpu itself, there will be communication overhead when the data operated on by the custom instruction get transferred back and forth between cpu/FPGA (being on-chip means this will be low). To make the exercise worthwhile the custom instruction has to do something that takes very many cycles on the cpu and either speeds it up or reduces the power consumed (the Catapult project at Microsoft has a rack of FPGA enhanced machines speeding up/reducing the power of matching search engine queries to documents).

A CPU+FPGA is like CPU+GPU, except that FPGAs are programmed at a much lower level, i.e., there is little in the way of abstraction between what the hardware does and what the coder sees.

Does the world need a FPGA attached to their cpu? Most don’t but there are probably a few customers who do, e.g., data centers with systems performing dedicated tasks and anybody into serious bit twiddling. Other considerations include Intel needing to add new bells and whistles to its product so that customers who have been trained over the years to buy the very latest product (which has the largest margins) stay on the buying treadmill. The FPGA is also a differentiator, not that Intel would ever think of AMD as a serious competitor.

Initially the obvious use case is libraries performing commonly occurring functionality. No, not matrix multiple and inverse, FPGA are predominantly integer operation units (there are approaches using non-standard floating-point formats that can be used if your FPGA unit does not have floating-point support).

From the compiler perspective the use case is spotting cpu intensive loops, where all the data can be held on the FPGA until processing is complete. Will there be enough of these loops to make it a worthwhile implementation target? I suspect not. But then I can see many PhDs being written on this topic and one of them could produce a viable implementation that bootstraps itself into one of the popular open source compilers.

Interpreters have to do a lot of housekeeping work. Perhaps programs written in Java or R could be executed on the FPGA that uses the cpu as a slave processor. It is claimed that most R programs spend their time in library functions that have been implemented in C and Fortran, but I’m seeing more and more code that appears to be all R. For some programs an R-machine implemented in hardware could produce orders of magnitude speed improvements.

The next generation of cryptocurrency proof-of-work algorithms are being designed to be memory intensive, so they cannot be efficiently implemented using ASIC-proof (this prevents mining being concentrated in a few groups who have built bespoke mining operations). The analysis I have seen is based on ‘conventional’ cpu and ASIC designs. A cpu+FPGA is a very different kind of beast and one that might require another round of cryptocurrency design.

These cpu+FPGA processors have the potential to dramatically upend existing approaches to structuring programs. Very interesting times ahead!

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Workshop on survival and time series analysis in empirical SE

February 12th, 2016 No comments

In January the material in my book on Empirical software engineering using R had its first exposure to professional software developers at a one day workshop (there was a rerun last week; slides here). The sessions were both fully booked, but as often happens on half turned up, around 15 at each workshop. A couple of people turned up expecting to be taught R and found themselves in a software engineering workshop that assumed the attendees could learn R in 10 minutes (because professional developers are experienced enough to learn the basics of programming in any language in that amount of time).

The main feedback was that people wanted to see more code, something to give them a starting point for the hands-on sessions (I hate seeing reams and reams of code on slides and had gone too far in the minimalist direction).

The approach of minimizing what developers have to learn/remember, even if it means using more computer resources (e.g., always using glm), went down very well.

The probability & statistics material, in the session before lunch, had a mixed reception; this is partly because I have not come up with a clear message and I’m trying to get over several disparate ideas, i.e., sampling issues, life is complicated and p-values. People were a bit unhappy about the life is complicated, data is messy and you have to think about what you are doing message.

I think the regression model building material was a hit. People saw the potential in fitting curves to data and found it easy to do once the parameters to plot, lines, glm and predict were sorted out. There was lots of interesting discussion around interpreting the fitted models, with me continually hamming on the point that p-value selection was a business risk factor decision and what constituted a good model depended on what it was going to be used for.

Those attending wanted to learn more, which is great since the main aim was to show people what useful things could be done to motivate them to go off and learn more (relatively little may be known about empirical software engineering, but there is more than can be fitted into a one day workshop)

There is a part-2 workshop in March and the plan is to cover survival analysis, time series analysis and if there is time something else. It will be assumed that people have the skill level of those who attended the first workshop, e.g., can write basic R and fit a simple regression model.

Thanks to The Rise for sponsoring the venue for all three workshops.

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subset vs array indexing: which will cause the least grief in R?

January 4th, 2016 9 comments

The comments on my post outlining recommended R usage for professional developers were universally scornful, with my proposal recommending subset receiving the greatest wrath. The main argument against using subset appeared to be that it went against existing practice, one comment linked to Hadley Wickham suggesting it was useful in an interactive session (and by implication not useful elsewhere).

The commenters appeared to be knowledgeable R users and I suspect might have fallen into the trap of thinking that having invested time in obtaining expertise of language intricacies, they ought to use these intricacies. Big mistake, the best way to make use of language expertise is to use it to avoid the intricacies, aiming to write simply, easy to understand code.

Let’s use Hadley’s example to discuss the pros and cons of subset vs. array indexing (normally I have lots of data to help make my case, but usage data for R is thin on the ground).

Some data to work with, which would normally be read from a file.

sample_df = data.frame(a = 1:5, b = 5:1, c = c(5, 3, 1, 4, 1))

The following are two of the ways of extracting all rows for which a >= 4:

subset(sample_df, a >= 4)
# has the same external effect as:
sample_df[sample_df$a >= 4, ]

The subset approach has the advantages:

  1. The array name, sample_df, only appears once. If this code is cut-and-pasted or the array name changes, the person editing the code may omit changing the second occurrence.
  2. Omitting the comma in the array access is an easy mistake to make (and it won’t get flagged).
  3. The person writing the code has to remember that in R data is stored in row-column order (it is in column-row order in many languages in common use). This might not be a problem for developers who only code in R, but my target audience are likely to be casual R users.

The case for subset is not all positive; there is a use case where it will produce the wrong answer. Let’s say I want all the rows where b has some computed value and I have chosen to store this computed value in a variable called c.

c=3
subset(sample_df, b == c)

I get the surprising output:

>   a b c
> 1 1 5 5
> 5 5 1 1

because the code I have written is actually equivalent to:

sample_df[sample_df$b == sample_df$c, ]

The problem is caused by the data containing a column having the same name as the variable used to hold the computed value that is tested.

So both subset and array indexing are a source of potential problems. Which of the two is likely to cause the most grief?

Unless the files being processed each potentially contain many columns having unknown (at time of writing the code) names, I think the subset name clash problem is much less likely to occur than the array indexing problems listed earlier.

Its a shame that assignment to subset is not supported (something to consider for future release), but reading is the common case and that is what we are interested in.

Yes, subset is restricted to 2-dimensional objects, but most data is 2-dimensional (at least in my world). Again concentrate recommendations on the common case.

When a choice is available, developers should pick the construct that is least likely to cause problems, and trivial mistakes are the most common cause of problems.

Does anybody have a convincing argument why array indexing is to be preferred over subset (not common usage is the reason of last resort for the desperate)?