Software Engineering Nederland

Awards - MOBILESoft 2020 Best Paper Award for VU researchers

At the 7th IEEE/ACM International Conference on Mobile Software Engineering and Systems (MOBILESoft 2020), the Best Paper Award was awarded to the study "Leave my Apps Alone! A Study on how Android Developers Access Installed Apps on User's Device" by Gian Luca Scoccia, Ibrahim Kanj (a VU Master student), Ivano Malavolta (member of the S2 group at the VU), and Kaveh Razavi (former-VU assistant professor).

The paper and replication package are available here:

PhD defences - Tukaram Muske defended his thesis on postprocessing static analysis alarms

Tukaram Muske has successfully defended a PhD thesis on postprocessing static analysis alarms.


Static analysis, which detects errors in the source code without actually running it, is an important automated program analysis technique to find common programming errors and report on points of interest that could be errors.
Considering the effectiveness and usefulness of static analysis, a wide range of static analysis tools have been developed. However, these tools are known to generate a large number of false alarms. Tukaram Muske has developed alarm postprocessing techniques that significantly reduce the time and effort needed to inspect those alarms manually.
Reducing the number of static analysis alarms is an important challenge that academia and industry are both working on. Reporting fewer alarms by suppressing a subset of alarms is dangerous, because it can lead to missing critical errors. Muske addresses the problem of large numbers of alarms by processing the alarms after they are generated: postprocessing.
The postprocessing techniques designed by Muske work regardless of the static analysis tool in use, and manage to reduce the number of alarms by up to 36% and the time required to automatically eliminate false positives by up to 60%. This could prove an important time and cost savings and enable faster and more accurate response to software errors.

Tukaram Muske has conducted his research at the Tata Research Development and Design Center (Pune, India). He has been supervised by Alexander Serebrenik and Mark van den Brand (Eindhoven University of Technology). The PhD thesis can be found online:

Next events - Call for Papers: Collective Knowledge in Software Engineering

Two VERSEN members, Ivano Malavolta and Alexander Serebrenik are among the co-editors of the upcoming special issue of Empirical Software Engineering on collective knowledge in software engineering.

Knowledge-sharing platforms, such as Stack Overflow, GitHub, Twitter, and Slack, have changed how developers share knowledge and seek information on the web. These platforms store a significant amount of collective knowledge that is contributed by a large and rapidly-evolving group of participants. For instance, Stack Overflow provides a question and answer (Q&A) platform for developers to share programming-related knowledge. As of 2020, more than 12 million developers from around the world have contributed to Stack Overflow. Through such a collective model, Stack Overflow has accumulated a tremendous amount of knowledge, including more than 19 million questions, 29 million answers, and 74 million comments. As another example, GitHub has accumulated more than 61 million software project repositories that are developed by more than 20 million developers. Developers collectively contribute various types of knowledge such as source code and issue reports that provide rich information to assist future software development. In addition to Stack Overflow and GitHub, developers also commonly share their knowledge on social media and communication platforms, such as Twitter and Slack. This massive amount of collective knowledge could be leveraged to significantly benefit the software engineering community. In fact, many recent studies leverage such collective knowledge (e.g., from Stack Overflow, GitHub, and Twitter) to uncover empirical evidence or develop research techniques to further improve the software development process and quality assurance practice. The empirical findings and techniques have shown great success in addressing various software engineering problems (e.g., code generation, code recommendation, debugging and repair, and API documentation enhancement). Therefore, collective knowledge in software engineering has attracted great attention in the software engineering and other research communities (e.g., data mining and AI).

We invite the submission of high-quality papers describing original and significant work in all areas of collective knowledge in software engineering, including but not limited to:

  • Improving the sharing and management of collective knowledge;
  • Approaches and techniques for knowledge discovery from collective software engineering knowledge; and
  • Leveraging collective knowledge to facilitate software engineering tasks.

Further information about the special issue, evaluation criteria and submission guidelines can be found on

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