Your code and AI – more than precision and recall!

Image by Daniel Hannah from Pixabay

Using machine learning and AI to improve your coding is an important area of research. Together with colleagues we work with these techniques, to take them from open source to more industry quality.

There are two great tools that one can use today already. One of the tools is a beta version of add-in for visual studio, which helps software engineers to write code.

https://www.microsoft.com/en-us/ai/ai-lab-code-defect

Microsoft is very active in this area and even has release a set of tools that support the development of AI systems: https://www.microsoft.com/en-us/research/project/visual-studio-code-tools-ai/

Also:

https://techcommunity.microsoft.com/t5/educator-developer-blog/visual-studio-code-tools-for-ai-extension/ba-p/379420

What is great is that the tools are, naturally, available freely!

Another tool is a DeepCode, which analyzes software code and provides suggestions to improve it – e.g. use a specific design pattern or refactoring.

https://www.deepcode.ai/

This is great that we have increasingly more tools and that AI engineering matures. We do not want to have precision and recall steer our development. We want to have real testing and real systems. We also need to work with data quality in order to ensure that the systems are reliable.

The alternative is that we use MCC, precision, recall, F1-score to tell us how good a system is, which is not entirely true. These measures do not provide any view on how the system reflects the requirements put on it. These measures allow us to compare different classifiers, but not systems.

I hope that we can focus more discussion on AI quality and not classification quality/accuracy.

Classifying code smells…

https://link-springer-com.ezproxy.ub.gu.se/article/10.1007%2Fs11219-020-09498-y

Image by Comfreak from Pixabay

Code smells are quite interesting phenomena to study. They are not really defects, but they are not good code either. They exist, but people rarely want to admit to them. There is also no consensus to how much effort it takes to remove them (or even whether they should be removed or just avoided).

In this paper, the authors study whether it is possible to use ML to find code smells. It turns out it is possible and the accuracy is quite high (over 95%). It also shows that sometimes it is better to show a number of recommendations (e.g. two potential smells) rather than one – it requires less accuracy to make the recommendation, but helps the users to narrow-down their solution spaces.

Machine learning for source code suggestion, completion

Image by StockSnap from Pixabay

https://www-sciencedirect-com.ezproxy.ub.gu.se/science/article/pii/S0950584920300616

This is a great paper demonstrating the use of NLP techniques for completion of software source code. It uses recurrent networks and can reduce the size of the vocabulary compared to previous approaches.

As the authors say: “The CodeGRU introduces a novel approach which can correctly capture the source code context by leveraging the token type information.”

I like the approach because it can extract the information that is important for the analysis of source code – what kind of token is analysed and how it is used.

Conclusions (quote from the abstract): “Our experiment confirms that the source code’s contextual information can be vital and can help improve the software language models. The extensive evaluation of CodeGRU shows that it outperforms the state-of-the-art models. The results further suggest that the proposed approach can help reduce the vocabulary size and is of practical use for software developers.”

I’m kind of keen to check this approach in our work. See if we can use this to improve the quality of source code.

Developing recommender systems – a framework which may just be the one (for us)…

https://rdcu.be/b3wgZ

Image by Gerd Altmann from Pixabay 

Creating recommendation systems is a tricky task. We need to add the temporal domain to the data. In particular, we need to make sure that we capture what was recommended before to the specific user and how the user reacted upon that. We also need to capture the evolution of the users and the data.

In this paper, the authors present a framework, RectoLibry, which helps to construct these kind of systems. The system captures both the parts of the development of the recommendations, but also their deployment.

The system is based on designing an ontology (yes, my old, good friend, used since before Web 2.0, even in my own research: https://link.springer.com/chapter/10.1007/978-3-540-87875-9_60 , https://link.springer.com/chapter/10.1007/3-540-46102-7_20 ).

The ontology describes the relationships existing in the recommendation domain and provide the support for the selections and feedback loops.

I recommend to take a look at the paper and the framework if you want to build a recommendation system. I will, when looking at the assignments from the software measurement PhD course.

Generating comments in code – can a machine make our code more readable?

Image by Markus Spiske from Pixabay 

Our research team has been working with code comments for a while now. We have done analyses of source code comments and the code that is commented. We have also worked with the needs for code comments.

The results showed that AI support for code reviewing process is very much needed. However, it has also shown that the current tools are not good enough yet. One of the tools is DeepCode.ai, which analyzes code repository and finds problems in the code. It is a great tool, but it has been trained on large data sets from open source projects, which makes it tricky to use on proprietary software. It does not know how to capture the project specific characteristics.

I’ve recently cam across this article (https://link-springer-com.ezproxy.ub.gu.se/content/pdf/10.1007/s10664-019-09730-9.pdf) which is about generating code comments. It can generate code comments (not review comments) for the Java programming language. It “understands” what the code does and generates a natural language description of the code. The tool is based on the latest work from the NLP domain and has been trained on over 44 million statements, which is quite a number😊

The tool is not perfect (yet), but shows improvement over existing approaches and is definitely opening up new alleys of using NLP in Software Engineering.

Let’s stay tuned, more will come!

Quality of Deep Learning code – article review

A (deep) Staircase in Vatican, Image by JEROME CLARYSSE from Pixabay

http://swat.polymtl.ca/~foutsekh/docs/hadhemi-MSR2020.pdf

Deep learning models are often designed, trained and tested in Python. It is a language with a nice structure, quite straigtforward syntax and a lot of libraries. However, very few tutorials about deep learning (or any Python programming tutorials) discuss the quality of the code, e.g. its modularization, encapsulation, naming consistency.

As a result, a lot of code for machine learning, written in Python, often is hard to read and hard to grasp. Even if used as part of jupyter notebooks, the code is not really commented (often).

The study behind the link above is a study that supports my long gut feeling about this. The findings show that (from the abstract): First, long lambda expression, long ternary conditional expression, and complex container comprehension smells are frequently found in deep learning projects. That is, deep learning code involves more complex or longer expressions than the traditional code does. Second, the number of code smells increases across the releases of deep learning applications. Third, we found that there is a co-existence between code smells and software bugs in the studied deep learning code, which confirms our conjecture on the degraded code quality of deep learning applications.

The second finding, about the constant increase of the number of code smells, is similar to the studies we did in proprietary software about complexity – the complexity “never” decreases ( http://web.student.chalmers.se/~vard/files/Monitoring%20Complexity%20Evolution.pdf ).

The study compares 59 deep learning systems with 59 non-ML systems from GitHub. One could argue that the sample is not representative (no propprietary systems), but it is a fair sample.

To sum up, a very nice reading, showing that we need to think about quality, not only models, but also code quality.