So, the holidays are over, a new year starts, new resolutions are made, new projects started. But before we get all stuck in the work, I’d like to share a book suggestion to read on the go.
This book is about the current trends in the modern world. It discusses such aspects as the our dependency on technology, the way we use it to produce food and to make things. It talks about how the current supply chains get disrupted and what we need to do to maintain/regain the balance.
Finally, it talks about the energy, our dependence on the oil energy and on the nuclear power.
However, this is not a doomsday book, quite a contrary. It is a book about the hope in the development of the modern society and how we should contribute to it. I strongly recommend this book as a reading for the evenings, after hearing about the energy crisis. I recommend to take this book in and reflect on the fact that we have achieved a lot and the world is not as scary as the news want it to be, or create it to be.
I hope that you will enjoy the book as much as I do.
I’ve read this book because I wanted to get some inspiration from social sciences. What I ended up with is a book about Bayesian statistics and its consequences. To some extent, I’m happy to have read it, because I got a better view of how to use Bayesian statistics in practice. Yet, I am a bit disappointed. Not about the statistics, but about the book.
A while back I read the book by Judea Pearl about Bayesian networks and this role in statistics. That book was about something new and a bit fresh. It got my interest and I felt refreshed after I read it. This book was a bit too much of a repetition. Don’t get me wrong, I like repetition and I like the style of Steven Pinker (he is one of my role models when it comes to academic writing).
The book is about making inferences by calculating probabilities using Bayes theorem. It explain them very well and shows how they are used and misused in modern society – from politics to medical diagnoses. The book shows a few tricks to make better decisions and inferences.
I guess I need to read it once more in order to get a better taste of its distinct flavor.
Modern software development organizations work a lot with metrics – they use them to focus their development effort, to understand their products, and, recently, to guide their online experimentation.
We’ve studied software measurement programs before, mostly from the perspective of what’s in the program – what is measured, how it is measured, who is responsible for the measurement and when the measurement is conducted.
In this article, we focus on the “softer” aspects of the measurement program – how mature is a metric team, and how mature is the organization where the metric team works. When studying four different companies, aka four different teams, we could observe that the maturity of the team goes hand-in-hand with the maturity of the organization. Mature organizations can take metrics and indicators into their workflows, and therefore increase the motivation to learn in the metrics team.
We’ve also found a few key factors for success of the metric team:
Formal, official mandate – which prevents questioning of the metric team, also important to make the team become a team, not a group of individuals
Link to the organization – a team needs to be part of a product or service development, they need to understand what is required from them.
Longevity – a team needs to be there for the long run, as the short term improvements will not last for very long (per definition), and therefore the motivation is not going to last.
I hope that this article, which is open access, will help others to understand how to evolve their teams and measurement programs.
I’m a big fan of the Matrix movies, but well, to be honest, who isn’t:) I like the scene where Morpheus gives Neo the choice of two pills – one to know the truth and the other one to go on living his life as previously.
Well, sometimes I feel the same when I do my programming tasks – do I really want to know what the code does, or just make a quick fix and move on? I would say that it’s 50-50 for me – sometimes I feel like contributing and sometimes I just fix the problem and move on.
In this paper, the authors conduct an experiment to understand how and when software developers make mistakes. They find that “[the] study suggests that a relatively high number of mistakes are related to communicating with stakeholders outside of the development team. “
Having worked with metrics teams all over the globe, I’ve noticed that the communication with the stakeholders is often the largest problem that you can have. The stakeholders don’t speak “requirements” and we do not understand “wants” of the stakeholders. But, well, it’s not what the paper is about.
What I like about the paper is the systematic approach to the study – using experiments and a technique for teaching the developers how to work with their limitations. This is what the authors recommend as remedies (quoted directly from the paper):
Know your own weaknesses. Every developer is different and struggles with different concepts. Our analysis shows a variety of types of errors that developers make. Developers becoming more conscious of the human errors they commonly make and actively checking for these can help reduce errors.
Use cognitive training. We have shown that using cognitive training, like the OODA loop, seems to help decision making and can reduce the human errors a developer makes.
Simplify your workload. One of the biggest causes of human error reported by the developers in our study was the complexity of the development environment. Reducing the cognitive load by simplifying the complexity of the development environment could reduce human errors. Actions such as minimizing the number of simultaneous development tasks and closing down unnecessary tools and windows can help reduce the cognitive load.
Communicate carefully with stakeholders outside your team. Our study suggests that a relatively high number of mistakes are related to communicating with stakeholders outside of the development team. Ensuring that communication is clearly understood seems important to reducing mistakes.
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.
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.
A while back I read an article in ZDNet about Linus Torvalds, the creator of Linux, and his daily work. He was (at the time of reading, which is ca. 2 years back) still working on the code. However, he was mostly working on the design of the system, reviewing patches and supporting younger designers. I’ve also read a number of articles which claimed the importance of code reviews as a way of teaching younger designers about the product and the code base.
In this paper, I’ve found that the support for younger designers is what the elite developers do a lot of. It seems that the communication, organisation and support are the activities that the elite developers find important. It’s aligned with what we do at the universities as well. The most elite professors work with students, show them how to program and how to structure their code. Seems like this is a very good way of continuing your career – help other be better.
I guess it’s time to change my wallpaper from “coding” to “teaching”….
Abstract: Open source developers, particularly the elite developers who own the administrative privileges for a project, maintain a diverse portfolio of contributing activities. They not only commit source code but also exert significant efforts on other communicative, organizational, and supportive activities. However, almost all prior research focuses on specific activities and fails to analyze elite developers’ activities in a comprehensive way. To bridge this gap, we conduct an empirical study with fine-grained event data from 20 large open source projects hosted on GITHUB. We investigate elite developers’ contributing activities and their impacts on project outcomes. Our analyses reveal three key findings: (1) elite developers participate in a variety of activities, of which technical contributions (e.g., coding) only account for a small proportion; (2) as the project grows, elite developers tend to put more effort into supportive and communicative activities and less effort into coding; and (3) elite developers’ efforts in nontechnical activities are negatively correlated with the project’s outcomes in terms of productivity and quality in general, except for a positive correlation with the bug fix rate (a quality indicator). These results provide an integrated view of elite developers’ activities and can inform an individual’s decision making about effort allocation, which could lead to improved project outcomes. The results also provide implications for supporting these elite developers.
I picked up this book to get some new perspective on research, work-life balance and, eventually, happiness. Not that I’m miserable, but I got intrigued by the recent developments in psychology and I wanted to take this as a bedtime reading. Midsummer reading, to be exact.
Well, the book is a great literature for that, no doubt about it. I like the style of the author and how he takes on examples. I also found that the book has two chapters about metrics and measurement. In chapter 2 and 3, the authors discusses our view on the science behind happiness and the fact that it’s very difficult (not impossible) to measure. There are ways to measure happiness, or estimate it.
What I like about the author’s approach is that he uses these measures to show temporal aspects of happiness – our estimations about how happy we will be, our happiness at the moment and finally our happiness after a while.
To sum up, the main point of the book is that happiness is what we create, not what we get.Just by changing the way we see things or how we compare things, can make us more happy.
Recently I’ve read a very interesting book about the disruption that happens in the banking sector. I’ve learnt that this is not the first book about the topic and I wanted to understand how things are actually working with AI and the banking sector.
We’ve published a paper a while back about the introduction to AI for bankers, but I wanted to know more about what has happened since then. The link to the paper is here: https://doi.org/10.2308/jeta-19-04-30-21
So, what’s new about this particular book? Or what’s new about the disruption?
The book talks a lot about the financial sector and how it evolved over the years. Bank 4.0 is a bank which is powered by AI and is able to reason about your economy at a higher abstraction level. Instead of asking “Siri, how much money do I have on my account?”, we can start asking questions like “Siri, can I affort buying the new Playstation 5?” and the assistant will answer that you can, but then you need to cut down on your vacation expenses and maybe replan the purchase of the new mobile phone (which is getting old).
A small disclaimer here: I use Siri as example, this has nothing to do with Apple services (at least nothing that I’m aware of).
Another interesting aspect of banking 4.0 is the fact that financial actors are more trustworthy than the banks. The young generation would rather interact with the actors like WeChat (https://metrics.blogg.gu.se/?p=407) than interact with the bank.
However, what I like best about the book is the fact that it problematizes the concepts related to disruption – for example how do you know that you are being disrupted? or when should a company start pivoting, or even whether pivoting or abandonment of the old model is possible in the company that is being disrupted.
Data veracity is a concept where we define the degree to which data corresponds to the true values. It comes from the metrological concept of “measurement trueness”, which is the degree to which the measurement quantifies the value correctly.
Well, that sounds very simple, but it is in fact quite complex. In our previous work, we scrutinized what it means to have veracious data in transport systems (https://ieeexplore.ieee.org/abstract/document/7535482). It turns out that “lying” is not the only option here.
In this book, the author looks into the way how things can be untrue. Sometimes deliberately by lying, sometimes by mistake. Sometimes, as we learn in the last chapter (with Brazilian aardvark), a mistake can actually end up being accepted as truth over time.
I recommend the book as it is written in a fantastic manner, providing examples from the real world (e.g. the alleged drone sightings over Gatwick in 2018). It even goes a bit further and discusses the need of replication of studies and that we should get more funding for making the scientific results more solid and robust.
