I’ve picked up this book to continue my reading into our ability to reason and understand the reality around us. I’ve described this journey in my previous posts, e.g. https://metrics.blogg.gu.se/?p=438
This book goes beyond the trust and explores how we can be wrong and how, sometimes, we should actually be wrong. It describes different models of being wrong and also whether it is good to be right or not.
What I find interesting is the systematic overview of the sources of errors and how we deal with it.
I recommend to take a look at the book as a bedtime reading to learn about our view on being wrong. It describes interesting situations and analyzes them from different perspectives.
Working with continuous integration is an exciting new filed. You get your code into the main branch directly. Well, that’s what the theory says. What you really get is feedback directly, at least the feedback from the automated checks for technical debt, testing and similar.
What you do not get quickly is the review of your code by your colleagues. In larger organizations, things like code reviews do not get prioritized. Therefore they tend to slow down software development rather than speed up!
In this paper, we set of to understand how to fix that. We used Gerrit as the tool to extract lines of code to review, instead of reviewing all of the lines. Here is a short video about this: https://play.gu.se/media/t/0_h7hx95d2
The abstract of the paper is included:
Code reviews are one of the first quality assurance tasks in continuous software integration and delivery. The goal of our work is to reduce the need for manual reviews by automatically identify which code fragments should be further reviewed manually. We conducted an action research study with two companies where we extracted code reviews and build machine learning classifiers (AdaBoost and Convolutional Neural Network — CNN). Our results show that the accuracy of recognizing code fragments that require manual review, measured with Matthews Correlation Coefficient, was 0.70 in the combination of our own feature extraction and CNN. We conclude that this way of combining automation with manual code reviews can improve the speed of reviews while providing organizations with the possibility to support knowledge transfer among the designers.
In the process of reviewing code, we can identify refactoring pretty easy. We read the code, try to understand it and provide comments. In the understanding phase we also get ideas about possible alternative – why is this done this way?
Now, when writing the comments, we rarely have the time to refactor the code. In CI, this process of reviewing comes when we commit the code to the main branch and therefore we expect this to be delivered and used soon. So, it’s too late to refactor, we need to do it in the next iteration.
But the next iteration is the same, we need to deliver new functions, not “golden plate” the existing code, deliver it to the main branch, etc. When is the time for refactoring then? How do we document the possibilities and use them when we have a bit of time?
In this work, the authors look at the commit messages and identify refactoring possibilities for that, complementing the static and dynamic analysis of code. The method presented in the paper is based on the analysis of code from open source projects, the refactoring applied to the code and the analysis of the QMOOD quality attributes that were related to these commits.
The following quote from the paper explains a bit how the gist of the extraction of the refactored code works:
Identifying refactoring rationale has two parts. The first part is the detection of the files that are refactored by developers in a commit. The second part is the identification of changes in the QMOOD quality attributes then comparing these changes with the information in the commit message.
For the first part, we used the GitHub API to identify the changed files in each commit. In the second part, we compared the QMOOD quality attribute values before and after the commit to capture the actual quality changes for each file. Once the changed files and quality attributes were identified, we checked if the developers intended to actually improve these files and quality attributes. In fact, we preprocessed the commit messages and we used the names of code elements in the changed files and the changed quality metrics as keywords to match with words in the commit message. Once the refactoring rationale is automatically detected using this procedure, we continue with the next step to find better refactoring recommendations that can fully meet the developer’s intentions and expectations. In case that no quality changes were identified at all then a warning will be generated to developers that the manually applied refactorings are not addressing the quality issues described in his commit message.
As part of my weekend reading I took on a book about cultures. Not a typical reading of mine, but I though I would give it a try. This book is about differences in cultures from the perspective of rules, laws, principles. It is a common knowledge that some societies are very relaxed (e.g. Scandinavia) whereas some are very strict (e.g. Singapore). Commonly, we also know that the loose societies are more creative, whereas the more disciplined ones are very, well, disciplined.
This book also makes a point that it’s not as simple as that. This is not a linear relationship and there is some golden middle. Just being a loose society does not guarantee innovativeness and just being strict does not guarantee discipline. People need a “lagom” (here is a good Swedish word, meaning just right, in the middle, just enough, not too much and not too little) set of rules and looseness in the society.
When I read the book I thought about well-functioning organisations. In the companies and teams that I visit (or used to, before the pandemic), I often saw teams that were working together well with some degree of looseness, but not completely without the rules. They tend to perform well and function well when all team members understand the goals and rules of the game. I’ve also seen teams that are not able to function at all. They do not respect each other, have no respect for rules and provide no support for each other. They are “too loose” and therefore they are groups, but not really teams. At the same time I saw teams where the narcissistic boss controls everything and everything needs to go through the boss. They do not produce much, trust me.
However, there is also one more aspect – it’s where you come from. I, for that matter, cannot work in a self-organising team. I just don’t know how to find my place. One of my friends told me once – “Either you lead, or you follow or you get the hell out of the way”. Well, I’m more for that kind of the rule. Following is nice and I like it, but self-organising is so-so.
I’m actually part of a self-organised team in one of my assignments. I don’t know what to do there, I rely on a friend from the team to tell me when is my turn to speak and if I should say something or not. He also tells me when it’s a good time to do things and when it’s just a discussion. I’m not providing the name of the friend, but I’m super happy that I have him!
To sum up, I really recommend to read this book as it provides a bit wider perspective on rules in societies than the most common books from the organisational theories. It is about a normal person like me trying to find a place in life (well, by now I would actually think that this is easier, but maybe I’m just wiser to realise certain things).
Thanks for listening and tun in for the next blog post.
Assigning defects is a task that is not so much fun. Companies need to do that, but the persons who do it often change as the task is quite labor intensive and tiresome. There is, of course, a significant body of research about this and here is one example of it.
What is interesting in this article is that the authors use temporal data about the defect reports to assign teams. From the abstract: “In this article, we describe a new BA approach that relies on two key intuitions. Similar to traditional BA methods, our method constructs the expertise profile of project developers, based on the textual elements of the bugs they have fixed in the past; unlike traditional methods, however, our method considers only the programming keywords in these bug descriptions, relying on Stack Overflow as the vocabulary for these keywords. The second key intuition of our method is that recent expertise is more relevant than past expertise, which is why our method weighs the relevance of a developer’s expertise based on how recently they have fixed a bug with keywords similar to the bug at hand. “
The method uses text similarity measures to match defects and performs better than existing methods based on the meta-parameters. What it means in practice is that the only thing that is needed is the actual defect description, or actually a failure report in order to make the predictions.
Very interesting work to apply, it seems that the entry level is not that high for new companies.