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The main goal of this thesis is to investigate, improve and extend the applicability of mutation testing. To seek the potential directions of how to improve and extend the applicability of mutation testing, we have started with a systematic literature review on the current state of how mutation testing is applied. The results from the systematic literature review have further guided us towards three directions of research: speeding up mutation testing; deepening our understanding of mutation testing; exploring new application domains of mutation testing.

Debugging application crashes is an expensive and time-taking process, relying on the developer’s expertise, and requiring knowledge about the system. Over the years, the research community has developed several automated approaches to ease debugging. Among those approaches, search-based crash reproduction, which tries to generate a test case capable of reproducing a given crash to make it observable to the developers, solely based on the stack trace included in the crash report.

Join the next SERG Reading Club 📖📚! On Thursday, June the 18th, 14:00 - 15:00 we come together on Zoom to discuss: “Building Theories from Multiple Evidence Sources” by Forrest Shull and Raimund L. Feldmann. (pdf: https://www.cin.ufpe.br/~fmcf2/Doutorado/2008-Guide%20to%20Advanced%20Empirical%20Software%20Engineering.pdf#page=343) Wanna join & you are not part of our Mattermost? Send an email to c.e.brandt /at/ tudelft.nl to get the Zoom link 😊

Continuous Delivery for Machine Learning (CD4ML) deals with the challenges of applying Continuous Delivery principles to ML systems to make the end-to-end process of developing and deploying them more repeatable and reliable. These systems are generally more complex than traditional software applications, and ML models are non-deterministic and hard to explain. In this talk we will discuss the challenges of testing and quality in ML systems, and share some practices for applying different types of tests to help overcome those issues.

Hello Everyone, I will be presenting my ongoing research this Thursday (11th June, 15:30 hours) as part of my Go/No-Go meeting. I will talk about key challenges in integration of software and security from developer’s perspective. I will also cover the work done during the first year of my phd and future research plan. Please feel free to join to provide your valuable feedback. The audio/video conferencing details will be shared later.

This presentation will be about my ongoing research at TU Delft. We have worked on using ML techniques for predicitng topics for Github repositories. Github added topic tagging functionality in 2017, but so far only about 2M public repositories have at least one tag. Topic tags define the main features, functionality, and the goal behind the creation of a software repository. In this research, we propose an automatic approach for predicting topics for software repositories using state-of-the-art machine learning techniques.

This talk discusses the results of my three months internship in the SERG team, done as part of my master thesis at the University of Namur. During this internship, I worked with Xavier Devroey and Pouria Derakhshanfar on JCrashPack, a benchmark for Java crash reproduction. More specifically, I explored the following question: Can we measure the difficulty for search-based crash reproduction to reproduce a crash using software quality metrics? Abstract: Search-Based Crash Reproduction (SBCR) aims to help developers in their debugging tasks by generating a test case that reproduces a specific crash based on its stack trace and the source code.

Join the next SERG Reading Club📚📖! On Friday, May the 29th, 14:00 - 15:00 we come together on Zoom to discuss the generic problem of distinguishing causality and correlation based on this paper: “Failure is a four-letter word: a parody in empirical research." Zeller, Andreas, Thomas Zimmermann, and Christian Bird. Proceedings of the 7th International Conference on Predictive Models in Software Engineering. 2011. plus these slides: http://darribas.org/gds15/slides/lecture_10.pdf Wanna join & you are not part of our Mattermost?

Mistakes in what a program’s source code actually does are inevitable. One of the potential causes are “atoms of confusion''; the smallest possible patterns of misinterpretable source code. These patterns and the misunderstandings and errors they can cause are explored in past studies in the C programming language. They are found to occur in many large projects and style guides. For this work, the existing tested set of atoms of confusion is translated into the Java programming language.