TechDebt 2020 PC Co-Chair

Michael Felderer is PC Co-Chair of TechDebt 2020 collocated with ICSE 2020.

Technical debt describes a universal software development phenomenon: design or implementation constructs that are expedient in the short term but set up a technical context that can make future changes more costly or impossible. Software developers and managers increasingly use the concept to communicate key tradeoffs related to release and quality issues. The goal of this two-day conference is to bring together leading software researchers, practitioners, and tool vendors to explore theoretical and practical techniques that manage technical debt.

The Managing Technical Debt workshop series has provided a forum since 2010 for practitioners and researchers to discuss issues related to technical debt and share emerging practices used in software-development organizations. A week-long Dagstuhl Seminar on Managing Technical Debt in Software Engineering has produced a consensus definition for technical debt, a draft conceptual model, and a research roadmap.

To accelerate progress, an expanded two-day working conference format has become essential. The third edition of the TechDebt Conference will be held jointly with ICSE 2020 in Seoul, South Korea, on May 25-26, 2020. The conference is sponsored by ACM SIGSOFT and IEEE TCSE.

SE 20 General Chair

Michael Felderer is General Chair of SE 20 and organizes the conference together with his organization team in Innsbruck from February 24 to 28, 2020. The conference includes several amazing tracks. The website of the conference is available online at https://se20.ocg.at/

 

PROFES Tutorial

Together with Mika Mäntylä and supported by Vahid Garousi and Austen Rainer, Michael Felderer gave a tutorial on Benefitting from Grey Literature in Software Engineering Research at PROFES 2019 in Barcelona.

Link

Description of the Tutorial: 

Grey literature is becoming more and more important as a source of knowledge because software engineering practitioners write and share information in different forms of grey literature (GL) like blogs, videos or white papers. The overall goal of this tutorial is to present ways how software engineering research can benefit from the vast amount of information covered by GL. The participants of this tutorial will learn how GL can be used for various aspects of software engineering research, e.g., shaping new directions of research, or using knowledge and evidence from grey literature in empirical studies in software engineering. First, the concept of GL in general and from the perspective of different disciplines like health sciences or social sciences are presented. Second, the concept of GL in software engineering and types of GL are presented and discussed with the participants. Third, ways how GL can be used in primary studies and secondary studies in software engineering are presented. The discussed application scenarios in primary studies comprise analysis of GL materials with a qualitative approach, analysis of GL with a quantitative approach, and reference of GL sources. In secondary studies, GL can be incorporated into multivocal literature reviews and grey literature reviews. The instructors will present their guidelines for these reviews and explore them with the participants of the tutorial. Finally, the challenges and benefits of using GL in software engineering are discussed. The examples presented during the tutorial are from the domains of software processes and software testing. It is sufficient if potential participants are interested in the topic and intend to use grey literature in their empirical studies in software engineering. Knowledge of systematic literature review and empirical studies in software engineering are and advantage but not required. Participants should bring their notebook or tablet to enable participation in the practical part.

 

Rare sound annotation using deep neural networks

The goal of this thesis is the development of a software prototype that can annotate dangerous sound events. The thesis includes building a pipeline that can detect special such events and developing a Deep Neural Network (DNN) that can classify sound events as dangerous.  Furthermore, the pipeline will provide a table-like file (e.g. CSV) which contains for each audio-file an entry with the filename, the probability, the classification and the timestamps (start and end) of the occurred sound-event. The software and thesis are developed in collaboration with MED-EL.

Supervisor: Michael Felderer