Is software architecture and code the same?

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Relationships between software architecture and source code in practice: An exploratory survey and interview – ScienceDirect

Software architecting is one of the crucial activities for a success of your product. There is a BAPO model, there B stands for Business and A for Architecture – and there is a good reason why it is on the second place. It should not dictate your business model, but it should support it.

Well, it is also good that the architecture comes before processes and organization. If software is your product, then it should dictate how you work and how you are organized.

But, how about the software code? For many software programmers and designers, the architecture is a set of diagrams which show logical blocks and software organization, but they are not the ACTUAL code, not the product itself. In one of our research project we study exactly that kind of problem – how to ensure that we keep both aligned, or more accurately, how we can use machine learning to keep the code and architecture synchronized.

Note that I use the word synchronized, not aligned or updated. This is to avoid one of many misconceptions about software architectures — that they are set once and for all. Such an assumption is true for architectures of buildings, but not software. We are, and should be, more flexible than that.

In one of the latest Information and Software Technology issues, I found this interesting study. It is about how architects and programmers perceive software architectures. It shows how architectures evolve and why they are often outdated. It is a survey and I really like where it’s going. Strongly recommend to read if you are into software architectures, programming and the technical side of software engineering….

Cybersecurity, security, and safety…

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During the spring semester, my students did great work looking into the security of a car’s electrical system. They managed to decode signals, understand high-level data, and managed to perform small changes in the car’s function.

It all sounds great as thesis project. Both the students and the company loved this project. It was challenging, it was new, it was useful. But I’m not writing this post about that. I want to write about what has happened, or not happened, after that.

In the months that came after the thesis, I decided to look into mechanisms for how to design and implement secure software. Being a programmer at the bottom, I turned to GitHub for help. I search for tools and libraries for secure software design. I know, I could have searched for something different, but let’s start there.

The results were :

Analysis frameworks:

There were more of these, but most of the same kind. I was a bit amazed by the fact that there is so little outside of web design. I also looked at some of the research in this area (no systematic review, I promised myself not to do one). There I found all kinds of work, but mostly theoretical. The areas of interest:

  • Cryptography: how to encode/decode information, keys, passwords.
  • Secure software design: mostly analysis of vulnerabilities
  • Secure systems: mostly about passwords and vulnerabilities.
  • Privacy: how to keep the private information hidden from third parties (kind of security, but mostly something else – I’m still waiting to understand what).
  • Legacy operations: how to make the software long-lived and provide it with secure infrastructure.
  • Infrastructure: security of the cloud environments, end-to-end security.

Since I worked with software safety, I thought that it would be very similar. However, it was not. The safety community discussed, mostly, standardization, hazards, risks. Very little about code analysis, finding unsafe code, etc. So, mostly something different.

I’ll keep digging and I will run a few experiments with some of my students to understand what the technology could be. However, I’m not as optimistic as I was at the beginning of my search.

Open or close – how we can leverage innovation through collaboration (book review)

Open: The Story of Human Progress : Norberg, Johan: Böcker

Progress and innovation are very important for the development of our societies. Software engineers are focused on the progress in technology, software, frameworks, and the ways to develop software.

This book is about openness and closeness in modern society. It is a story showing how we benefit from being open and collaborative. I could not stop myself from making parallels to the original work about open software – “The Cathedral and The Bazaar” by Eric Raymond. Although a bit dated, the book opened my view on the open source movement.

We take for granted that we have Linux, GitHub, StackOverflow and all other tools for open collaboration, but it wasn’t always like that. The world used to be full of proprietary software and software engineers were people who turned requirements into products. It was the mighty business analysts who provided the requirements.

Well, we know that this does not work like that. Software engineers are often working on product – they take ownership of these products, they feel proud to create them. It turns out that the openness is the way to go here – when software engineers share code, they feel that they contribute to something bigger. When they keep the code to themselves, … well, I do not know what they feel. I like to create OSS products, docker containers and distribute them. Kind of feels better that way!

How to make your pull request merged quickly… (article highlight)

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How Developers Modify Pull Requests in Code Review | IEEE Journals & Magazine | IEEE Xplore

I must admit that I’m not the greatest contributor to OSS projects. Yes, I did a few of those and contributed to projects, but this is more like a hobby than a real work. My goal for 2022 is to make it better and even put together some docker containers to make my scripts more reusable. I even bought a book about Docker, which I’ve read and (theoretically) I’m good to go.

Anyways, I stumbled upon this work which is about how developers make good pull requests. The paper has examined OSS projects and found that you need to make a clear change as part of the pull request, you need to make a clear classification of that change and then you have a high chance that the pull request will be adopted soon.

Stay tuned for more on code reviews…

Predicting defect, but continuously (article highlight)

Continuous Software Bug Prediction (

Although a lot has been written about predicting defects, the problem is still valid. Some systems have more defects than others. In academia, we can do two things – educate young engineers in making better software or construct models for predicting where and when to find defects.

A lot of work in the defect prediction model development focuses on more-or-less randomly found releases. However, software development is not random, but structured, and often, continuous. This means that it’s important to understand that not all defects are found in the same release/path/commit as they are introduced (BTW: there is a lot of work on this aspect too).

In this work, the authors analyze 120 continuous releases of six software products and demonstrate the value of their prediction models. The novelty of this approach is a system that checks whether the releases are similar to one another based on the distributional characteristics. This means that the prediction models are tuned to each release based on these characteristics. These characteristics are, mostly, well-known metrics like the average cyclomatic complexity of a file, a MaxInheritanceTree of a class, etc. So – easy to collect and analyze, a lot of tools can be used for that.

The results, in short, show that the new method is better than randomly choosing a release or bagging releases. The results differ per project, but the approach is better than the other two, across the board.

I like the approach and will try it the next time I get my hands on software defects, issues, challenges. Let’s see when that happens:)

Guiding the selection of research methodologies (article highlight)

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Guiding the selection of research methodology in industry–academia collaboration in software engineering – ScienceDirect

Research methodology is something that we must follow when conducting research studies. Without a research methodology, we just search for something and if we find it, we do not know if this finding is universal, true, or even if it really exists…

In my early works, I got really interested in empirical software engineering, in particular in experimentation. One of the authors of this article was one of my supervisors and I fell for his way of understanding and describing software engineering – as an applied area of research.

Over time, I realized that experimentation is great, but it is still not 100% what I wanted. I understood that I would like to see more collaboration with software engineers in the industry, those who make their living by programming, architecting, testing, modifying the code. I did a study at one of the vehicle manufacturers in Sweden, where I studied the complexity of the entire car project. There I understood that software engineering needs to be studies and practices in the industry. Academia is the place where we shape young minds, where we can gather multiple companies to share their experiences, and where we can make findings from individual cases into universal laws.

In this article, the authors discuss research methodologies applicable for industrial, or industry-close research. They discuss even one of the technology transfer models as a way of research co-production and co-validation.

The authors conclude this great overview in the following way (from the conclusions):

When it comes to differences, the three methodologies differ in their primary objective: DSM on acquiring design knowledge through the design of artifacts, AR on change in socio-technical systems, and TTRM on the transfer of research to industry. The primary objective of one methodology may be a secondary objective in another. Thus, the differences between them are more in their focus than in which activities they include.

In our analysis and comparison of their feasibility for industry–academia collaboration in software engineering research, the selection depends on the primary objective and scope of the research (RQ3). We, therefore, advice researchers to consider the objectives of their software engineering research endeavor and select an appropriate methodological frame accordingly. Furthermore, we recommend studying different sources of information concerning, in particular, the chosen research methodology to better understand the methodology before using it when conducting industry–academia collaborative research.

I will include this article as mandatory reading in my AR Ph.D. course in the future.

The speed of light and laws of software engineering…

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While on vacation, I managed to watch a number of sci-fi movies, which I wanted to watch but did not have the time during the academic year. This got me thinking about certain laws of physics and the laws of software engineering. I think there are many similarities, and let me start by considering the speed of light, as a starter.

First of all, what we know about the speed of light is that it’s the fastest we know and that Einstein’s theory of relativity says that we cannot travel faster than the speed of light. Even if we were, such a speed is tremendously difficult.

  • How would we know where we travel if we cannot see where we travel (we go as fast as we can see, literally). So, the travel would be very fast and, probably, very short.
  • If we, somehow, see where we go, or detect an obstacle (e.g. by knowing its predicted position based on prior observations), how can we steer? We’re going so fast, that for the majority of the travel time, we would be going in straight lines. These straight lines would be similar to either the hyperjumps from Star Wars or the clicks from the Guardians of the Galaxy.
  • If we’re literally the fastest objects, how can others see us and avoid is? Is it possible to avoid the light? No, it’s not possible. This means that we would be super-chaotic.

Therefore, I think that, even if we could, traveling at the speed of light is probably not the best idea. At least not conceptually, which may change as we change.

How does it affect the laws of software engineering then. Well, I would start with the laws of complexity.

For a while now, I’ve been broadcasting the opinion that the complexity of software cannot be reduced, it can only be hidden. For complex problems, we need complex software and complex software cannot be simple. If our algorithms have many conditions, we cannot take them away, we can hide them in functions, but never get rid of them. That’s the first parallel – we cannot travel faster than the speed of light.

We can hide complexity, and thus make the program/software easier to understand and maintain, but the better we hide it, the harder it is to avoid/predict complexity. Packaging complex algorithms in simple blocks will make it difficult to make modifications. Actually, not to make the modifications, but to overview the consequences of these modifications.

If we simplify the program/algorithm too much, we need to expect that it’s going to provide erroneous results for some cases – again, complex problems require complex programs. An example of such issue is approximating continuous functions – since our computers are discrete, there is always some degree of error in such an approximation.

Finally, interconnectivity and modularity as a means of handling complexity have their limits. I do not think we can develop increasingly complex software by increasing its size. I believe it’s going to be difficult in the long run. We need to make sure that we have the competence to handle complexity and we need to be able to make the complexity apparent.

Comparing different security vulnerability detection techniques (article review)

Image by Reimund Bertrams from Pixabay

An Empirical Study of Rule-Based and Learning-Based Approaches for Static Application Security Testing (

In the recent weeks I’ve turned into a specific part of my work, i.e. security vulnerability detection. In many areas, working with security has focused on the entire chain. And that’s a good thing – we need to understand when and where we have a vulnerability. However, that’s not what I can help with, which has never really stopped me before.

So, I was looking for more programmatic view on security. To be more exact, I wonted to know what we, as software engineers, need to focus on when it comes to cyber security. We can, naturally, measure it, but that’s probably not the only thing. We can analyze libraries from OSS communities to find which ones could be exploited. We can even program in a specific way to minimize the risk of the exploitation.

In this paper, the authors compare two different techniques for software vulnerability prediction – static software analysis and vulnerability prediction models. They have identified 12 different findings, of which the following are the most interesting ones:

  • SVP models are generally a bit better when it comes to precision and overall preformance.
  • SVP models provide fewer files to inspect as the output, which saves the cost.
  • The two approaches lack synergy, and it’s difficult to use them together to increase their performance.

Since they have compared only a few tools, I believe it’s important to do more experiments. It is also important to understand whether it is good or bad to have fewer files to inspect – I mean, one undetected vulnerability can be very costly…

autoML – let’s talk about it…

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AutoML, a promise of green pastures, less work, optimal results. So, it is like that? In this post I share my view on this and experience from running the first test using that model.

First of all, let’s be honest, there is not such thing as a free lunch. In case of autoML (auto-sklearn), the price tag comes first with the effort, skills and time to install it and make it work. The second is the performance…. It’s painfully slow compared to your own models, simply because it tests a lot of models here and there. It also take a lot of time to download and to make it work.

But, first thing first, let me tell you where I start. So, I used the data from the MicroHRV project ( 3. MicroHRV: Recognizing Rare Events in Microwave Radio Links and Intensive Care Units using Machine Learning – Software Center ( The data is from patients being operated to remove clots of blood from the brain (although dangerous it may sound, the actual procedure is planned and calm). I wanted to check whether autoML can do better compared to what we have at the moment.

What we have at the moment (for that particular dataset) is: Accuracy: 0.98, Precision: 0.98, Recall: 0.98 – using Random Forest classifier. So, this is actually already very good. For the medical domain, that’s actually in class of its own, given our previous studies ended up with ca. 0.7 in accuracy at best.

When it comes to installing autoML – if you like stackoverflow, downgrading, upgrading, compiling, etc. and run Windows 10, then it’s your heaven. If you run Linux – no problems. Otherwise – stick to manual analyses:)

After two days (and nights) of trying, the best configuration was:

  • WSL – Windows Subsystem for Linux
  • Ubuntu 20, and
  • countless of oss libraries

It takes a while to get it to work, the question is whether the results are good enough…

After three hours of waiting, a lot of heat from my laptop, over 1,000 models tested resulted in Accuracy: 0.91, Precision: 0.94, Recall: 0.91

So, worse than my manual selection of models. I include the confusion matrices.

Random forest

The matrices are not that different, as the validation sets are not that large either. However, it seems that the RF is still better than the best model from autoML.

I need work more on that and see if I do something wrong. However, I take this as a success – I’m better than autoML (still some use of an old professor) – instead of a let-down of not getting better results.

By the end of the day, 0.98 in accuracy is still very good!

Reproducing AI models – a guideline

Image by Pete Linforth from Pixabay

2107.00821.pdf (

Machine learning has been used in software engineering as a great tool for both research and development. The fact that we have access to TensorFlow, PyCharm, and other toolkits, provides almost endless possibilities. Combine that with the hundreds (if not thousands) of datasets from Zenodo and Co. and you can train a model for almost anything.

So far, so good, I would say. Problems (yes, there are always some problems) appear when we want to reproduce the results of others. Training a model on your own dataset and making it available is easy. Trusting such a model in a new context is not.

Imagine an example of an ML model trained on data from Company X. We have probably tuned the parameters a lot, so the model works great there, but does it work for Company Y? Most probably it will not. Well, it will work, but the performance of the predictions are not going to be great.

So, Google has partner up with academic partners to set up SIGMODELS, and TensorFlow garden, initiatives that are aimed at making ML models more portable, experiments more replicable, and all the other goodies.

In this paper, the authors provide a set of checks, which we can use to make the models more transparent, which is the first step towards reproducibility. In these guidelines, the authors advocate for reporting the models architecture, their input and output structure, building blocks, loss functions, etc.

Naturally, they also recommend to report metrics which were used to optimize the models, e.g. accuracy, F1-score, MCC or others. I know, these are probably essentials, but you would be surprised to see that many authors do not really report these metrics. If they are omitted, then how do we know if the metrics were just so poor that the authors omitted them (low performance of the model) or that they are not relevant (low relevance of the metrics – which is a good thing).

For now, these guidelines are only a draft, but I hope that they will become more mainstream. just like the emprical guidelines from ACM (GitHub – acmsigsoft/EmpiricalStandards: Empirical standards for conducting and evaluating research in software engineering).