Tag Archives: author preparation

Research Proposal #1: The Effects of Author Preparation in Peer Code Review

The Problem Space

Click here to read about my problem space

Related Work

During his study at Cisco Systems, Jason Cohen noticed that review requests with some form of author preparation consistently had fewer defects found in them.

Jason Cohen explains what author preparation is…

The idea of “author preparation” is that authors should annotate their source code before the review begins.  Annotations guide the reviewer through the changes, showing which files to look at first and defending the reason and methods behind each code modification.  The theory is that because the author has to re-think all the changes during the annotation process, the author will himself uncover most of the defects before the review even begins, thus making the review itself more efficient.  Reviewers will uncover problems the author truly would not have thought of otherwise.

(Best Kept Secrets of Peer Code Review, p80-81)

Cohen gives two theories to account for the drop in defects:

  1. By performing author preparation, authors were effectively self-reviewing, and removed defects that would normally be found by others.
  2. Since authors were actively explaining, or defending their code, this sabotaged the reviewers ability to do their job objectively and effectively.  There is a “blinding effect”.

In his study, Cohen subscribes to the first theory.  He writes:

A survey of the reviews in question show the author is being conscientious, careful, and helpful, and not misleading the reviewer.  Often the reviewer will respond to or ask a question or open a conversation on another line of code, demonstrating that he was not dulled by the author’s annotations.

While it’s certainly possible that Cohen is correct, the evidence to support his claim is tenuous at best, as it suffers from selection bias, and has not been drawn from a properly controlled experiment.

What do I want to do?

I want to design a proper, controlled experiment in an attempt to figure out why exactly the number of found defects drop when authors prepare their review requests.

My experiment is still being designed, but at its simplest:

We devise a review request with several types of bugs intentionally inserted.  We create “author preparation” commentary to go along with the review request.  We show the review request to a series of developers – giving some the author preparation, and some without – and ask the developers to perform a review.

We then take measurement on the number/type/density of the defects that they find.

Why do you care?

If it is shown that author preparation does not negatively affect the number of defects that the reviewers find, this is conclusive evidence to support Cohen’s claim that author preparation is good.  This practice can then be adopted/argued for in order to increase the effectiveness of code reviews.

On the other hand, if it is shown that author preparation negatively affects the number of defects that the reviewers find, this has some interesting consequences.

The obvious one is the conclusion that authors should not prepare their review requests, so as to maximize the number of defects that their reviewers find.

The less obvious one takes the experimental result a step further. Why should this “blinding effect” stop at author preparation?  Perhaps a review by any participant will negatively affect the number of defects found by subsequent reviews?  The experiment will be designed to investigate this possibility as well.

Either way, the benefits or drawbacks of author preparation will hopefully be revealed, to the betterment of the code review process.

Author Preparation in Code Review: What Are Those Authors Saying?

If you recall, I’m looking at author preparation in code review, and whether or not it impairs the ability of reviewers to perform objective reviews effectively.

If this is really going to be my research project, I’ll need to get my feet a bit more wet before I design my experiment.  It’s all well and good to say that I’m studying author preparation…but I need to actually get a handle on what authors tend to say when they prepare their review requests.

So how am I going to find out the kinds of things that authors write during author preparation?  The MarkUs Project and the Basie Project both use ReviewBoard, so it’ll be no problem to grab some review requests from there.  But that’s a lot of digging if I do it by hand.

So I won’t do it by hand.  I’ll write a script.

You see, I’ve become pretty good at manipulating the ReviewBoard API.  So mining the MarkUs and Basie ReviewBoard’s should be a cinch.

But I’d like to go a little further. I want more data.  I want data from some projects outside of UofT.

Luckily, ReviewBoard has been kind enough to list several open source projects that are also using their software.  And some of those projects have their ReviewBoard instances open to the public.  So I just programmed my little script to visit those ReviewBoard instances, and return all of the review requests where the author of the request was the first person to make a review.  Easy.

Besides MarkUs and Basie, I chose to visit the Asterisk,  KDE, and MusicBrainz projects.

Asterisk was a crapshoot – of all of their review requests, not a single one returned a positive.

But I got a few blips on the others. Not many, but a few.

I read all of the author preparation for each blip, and broke down what I read into some generalizations.

So, now to the meat:  here are some generalizations of what the authors tended to say, in no particular order.  I’ve also included a few examples so you can check them out for yourselves.

“Here’s why I did this”

The author makes it explicit why a change was made in a particular way.

Examples:

“Here’s what this part does…”

The author goes into detail about what a portion of their diff actually does.

Examples:

“Can I get some advice on…”

The author isn’t entirely sure of something, and wants input from their peers.

Examples:

“Whoops, I made a mistake / inserted a bug.  I’ll update the diff.”

The author has found a mistake in their code, and either indicates that they’ll update the diff in the review request, or change the code before it is committed.

“Whoops – that stuff isn’t supposed to be there.  Ignore.”

The author has accidentally inserted some code into the diff that they shouldn’t have.  They give their assurances that it’ll be removed before committing – reviewers are asked to ignore.

Examples:

“Before you apply this patch, you should probably…”

The author believes that the reviewers will need to do something special, or out of the ordinary, in order to apply the diff.

“…hello?”

The review request has been idle for a while without a single review.  The author pings everybody for some attention.

Examples:

Anyhow, those are the general patterns that stand out.  I’ll post more if I find any.

Have you seen any other common patterns in author preparation?  What would you say, if you were preparing your code for someone else to review?  I’d love to hear any input.

PS:  If anyone is interested in getting the full list of author prepared review requests for these 4 projects, let me know, and I’ll toss up all the links.

The Importance of First Impressions: How Theatre Criticism Might Inform Peer Code Review

Discussion Plays

I have seen plays that have very clear stories, and very clear plots.  I leave the theatre knowing what has happened, and I can be pretty confident that the people who sat around me in the theatre all got the same message as I did.

I have also seen plays that are completely the opposite.  There doesn’t appear to be a story.  There doesn’t appear to be plot.  There are no real characters.  For these plays, all of a sudden, I have to do the work in order to make sense of it all.  And you can be pretty sure that every single audience member got something different out of it.

I want to talk about this second kind of play.  For now, I’m going to call this kind of play a discussion play, because for me, the best part about these kinds of plays is the discussion I have with my friends afterwards. We’ll all sit down in a restaurant or a cafe, order some food, and try to figure out what the hell we just saw.  Theories are tossed around.  Everybody brings their own unique impressions and observations to the table.  A very rich ecosystem of ideas develops.

Back to Peer Code Reviews

(trust me, this all ties together in the end)

When Jason Cohen did his Peer Review at Cisco Study, he noticed that code that had been prepared by the author for review seemed to have a lower defect density than code that had not been prepared.

What do I mean by prepared?  I’ll let Jason Cohen explain:

The idea of “author preparation” is that authors should annotate their source code before the review begins.  Annotations guide the reviewer through the changes, showing which files to look at first and defending the reason and methods behind each code modification.  The theory is that because the author has to re-think all the changes during the annotation process, the author will himself uncover most of the defects before the review even begins, thus making the review itself more efficient.  Reviewers will uncover problems the author truly would not have thought of otherwise.

(Best Kept Secrets of Peer Code Review, p80-81)

Looking at the data, author preparation does seem to have a palpable effect.  As Cohen notes, “for all reviews with at least one author preparation comment, defects density is never over 30; in fact the most common case is for there to be no defects at all!”.

The study has two explanations for this:

  1. Authors gave their code such a thorough look while annotating them, that most defects were eliminated right off the bat.
  2. Since authors were actively explaining, or defending their code, this sabotaged the reviewers ability to do their job effectively.

Cohen buys into the first explanation.  He writes:

A survey of the reviews in question show the author is being conscientious, careful, and helpful, and not misleading the reviewer.  Often the reviewer will respond to or ask a question or open a conversation on another line of code, demonstrating that he was not dulled by the author’s annotations.

I have huge respect for this study.  But I don’t entirely buy this explanation.  As Cohen later mentioned in an email to me, this conclusion is not derived from a controlled experiment, and also suffers from selection bias.

Back to those Discussion Plays

One of the worst things that can happen to me before going into a discussion play is for someone who has already seen it to tell me their impressions of what they thought was going on.  As soon as I hear their opinion, my own objectivity is compromised.  Whether I want to or not, I’ll have their impressions in the back of my mind, and I’ll be using it as a measuring stick or reference point for my own opinions and critiques. They’ve carved a cognitive path through the work, and I’m doomed to notice that path, and react to it.

This is horrible.  This limits me.  This more or less hobbles my ability to contribute something unique to the pool of ideas and criticisms in the after-play discussion.  Every impression I have is tainted by someone else’s first impression.

Don’t get me wrong – I love hearing about everyone’s impressions.  But after I have formed my own. This way, I believe we cover more ground.  A group of us watching a discussion play will carve unique cognitive paths through the work without influencing one another.  When we finally open up and present these paths and ideas to one another over food and drink, I believe we cover more ground.

I have no data to back this up.  Only years of theatre-going experience.

A Code Review Anecdote

I recently received an email from a colleague of mine.  She wanted me to go over some of her Javascript to make sure it was up to snuff, since she was relatively new to the language.  I noticed that she had also sent a copy of the email to another developer who has pretty sharp Javascript chops.

When I finally had some free time, I went back to her email to write up the review.  I felt bad – it was late, and the other reviewer hadn’t made a peep on the email thread, and she was hoping to use the code relatively soon.  So I dove in, wrote my review, and sent it off.

A little while later, the other developer sent me his review, saying:

And here was my answer, which I didn’t send to you so as not to influence your reply.  😉

So the author of the code received two unique reviews, and neither of them had influenced the other.  When I read his review, I noticed that we covered some similar ground, but a lot of unique ground as well.  I suspect this wouldn’t have been the case had he sent his review to me first.

The Hypothesis

I hypothesize that author preparation in code review sabotages reviewers abilities to objectively carve their own unique cognitive paths through the code.  They see things from the author’s point of view, and this dulls their critical eye.  Because of this, I believe fewer defects are detected.

I will take this hypothesis one step further.

I suspect any review, by the author or otherwise, will taint future reviews.  If someone has already reviewed some code, I suspect this review will impact and possibly limit the ability of other reviewers to look at the code objectively.  Like author preparation, I suspect this prevents reviewers from getting their own unique, valuable first impressions of the code.  And I suspect that this causes some defects to go undetected.

Testing This Hypothesis

It’s a simple idea really.  Take a chunk of code, and get some number of developers to review it.  Take this same code, add some author preparation comments, and get more developers to review it.  Do all of the normal balancing, etc.

The question:  do the number of detected defects drop?  If so, this looks like evidence that author preparation sabotages review ability.

Take the experiment one step further.  Take some code, have someone else review it, and then have participants review this code, having seen the first review.  What happens to the number and type of defects that they find?  What happens if they don’t see that initial review?  What yields high defect detection?

Sounds doable.  Sounds interesting.  Sounds like something that would answer a few questions.

Implications and Ideas

So what if one or both of my hypotheses are true?  What does this mean for peer code review?

Well, if author preparation alone sabotages review ability, then the answer is simple:  don’t let the authors prepare the review.  The code goes up, and they stay silent.

But what if both are true?

An idea:  how about I tweak MarkUs’s ReviewBoard so that reviewers cannot see what other reviewers have said until they’ve given one review?  What would happen to the defect detection numbers?  Would reviewers react negatively to this?  Would there be lots of repetition in the comments?  Sounds like something worth looking into.

I’d love to hear some thoughts on this.  Anyone?