News

Distinguished Artifact Award received at the ICSME 2020

01.10.2020
We are happy to announce that our group members Alexander Trautsch, Steffen Herbold and Jens Grabowski received a Distinguished Artifact Award for the artifact supplementing their paper "Static source code metrics and static analysis warnings for fine-grained just-in-time defect prediction" at the 36th International Conference on Software Maintenance and Evolution (ICSME).

Latest TDL specifications published

10.09.2020
The outcomes of our continued work on the Test Description Language (TDL) within STF 577 of the European Telecommunications Standards Institute (ETSI) have been published as updated versions of the specifications:The latest version of the technical report on the reference implementation of TDL is expected to be published soon as well.

Paper accepted at SAM 2020

04.09.2020
We are happy to announce that our paper Facilitating the Co-Evolution of Semantic Descriptions in Standards and Models is accepted for presentation at the 12th System Analysis and Modelling Conference (SAM 2020). The conference will take place on October 19-20th in conjunction with the ACM / IEEE 23nd International Conference on Model Driven Engineering Languages and Systems (MODELS 2020), both of which will be held online due to the current circumstances (originally scheduled to be held in Montreal, Canada).

Two papers accepted at Empirical Software Engineering

26.08.2020

We are happy to announce that two of our papers were recently accepted for publication in the Empirical Software Engineering journal. 


The paper A Longitudinal Study of Static Analysis Warning Evolution and the Effects of PMD on Software Quality in Apache Open Source Projects is joint work by Alexander Trautsch, Steffen Herbold, and Jens Grabowski and addresses research questions regarding the evolution of the internal software code quality with respect to warning of static analysis tools. Our study shows that, on average, the code quality improved in the last two decades, mainly due to better coding guidelines, that, e.g., prohibit if-statements without an explicit if-block with {}. These quality improvements seem to be largely independent from the active use of static analysis tools within the build pipeline the projects. 

The paper On the Feasibility of Automated Prediction of Bug and Non-Bug Issues is joint work by Steffen Herbold, Alexander Trautsch, and Fabian Trautsch and deals with the question if machine learning can help to identify which issue reports describe bugs. We find that state-of-the-art models are almost as accurate as software developers and that such models, especially if trained on huge amounts of data, tend have a very high recall, i.e., hardly miss bugs. 

Paper accepted at the Journal of Systems and Software

26.08.2020
We are happy to announce that our paper A Systematic Mapping Study of Developer Social Networks was accepted for publication of the Journal of Systems and Software. The paper is joint work of Steffen Herbold, Aynur Amirfallah, Fabian Trautsch, and Jens Grawboski and presents the results of our literature survey regarding developer social networks in research that covers research topics, data sources and scope of experiments, influential papers and authors, as well as recent trends. 

Paper accepted at Expert Systems with Application

26.08.2020
We are happy to announce that the article A Multi-Objective Anytime Rule Mining System to Ease Iterative Feedback from Domain Experts was accepted for publication in the Journal Expert Systems with Applications. The article was joint work by Tobias Baum, Steffen Herbold, and Kurt Schneider and discusses a genetic algorithm, that can be used to learn rules in disjunctive normal form, such that experts not only understand the outcome of the learning process, but are directly able to influence the result of the algorithm through the interactive definition of restrictions on the solution space. 

Paper accepted at the ICSME Research Track

10.08.2020
We are happy to announce that our paper Static source code metrics and static analysis warnings for fine-grained just-in-time defect prediction was accepted for publication at the Research Track of the 36th International Conference on Software Maintenance and Evolution.The paper reports the results of a large-scale study of 38 open source projects over 15 years of project history. We investigated whether a fine-grained just-in-time defect prediction approach can benefit from additional static source code features as well as static analysis warnings.It includes an evaluation of different feature sets on two machine learning algorithms, an advanced SZZ algorithm to find bug-inducing changes and a specialized cost model for defect prediction.It is the largest study to date that includes static source code metrics and the first study to include static analysis warning density based features in just-in-time defect prediction.

Article accepted at the 13th System Testing and Validation (STV'20) workshop

10.07.2020
We are happy to announce that our article Using TDL for Standardised Test Purpose Definitions is accepted at the 13th IEEE International Workshop on System Testing and Validation (STV'20), co-located with 20th IEEE International Conference on Software Quality, Reliability, and Security (QRS 2020). Within this article, we discuss the Structured Test Objective Specification extension for the Test Description Language (TDL) and its application in several contexts related to standardised test purposes.

Philip Makedonski elected as member of the board of the Institute of Computer Science

10.07.2020
We are happy to announce that Dr. Philip Makedonski has been elected as a member of the board of the Institute of Computer Science.

Registered Report accepted at the MSR

01.04.2020
We are happy to announce that our study protocol Large-Scale Manual Validation of Bugfixing Changes has been accepted at the Registered Reports track of the 2020 IEEE/ACM 17th International Conference on Mining Software Repositories (MSR 2020). The protocol describes how we want to validate a large amount of bug fixing commits to create a new and detailed data set about bug fixing changes that can be used by researchers for different purposes, e.g., defect prediction, program repair, or bug localization. Our goal is to conduct this project together with the community and we invite other researchers to contribute. The protocol is already registered in the OSF Registry

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