EASE 2024
Tue 18 - Fri 21 June 2024 Salerno, Italy
Thu 20 Jun 2024 11:15 - 11:30 at Room Capri - Defects Chair(s): Davide Falessi

Context: Software defect prediction utilizes historical data to direct software quality assurance resources to potentially problematic components. Effort-aware (EA) defect prediction prioritizes more bug-like components by taking cost-effectiveness into account. In other words, it is a ranking problem, however, existing ranking strategies based on classification, give limited consideration to ranking errors. Objective: Improve the performance of classifier-based EA ranking methods by focusing on ranking errors. Method: We propose a ranking score calculation strategy called EA-Z which sets a lower bound to avoid near-zero ranking errors. We investigate four primary EA ranking strategies with 16 classification learners, and conduct the experiments for EA-Z and the other four existing strategies. Results: Experimental results from 72 data sets show EA-Z is the best ranking score calculation strategy in terms of Recall@20% and Popt when considering all 16 learners. For particular learners, imbalanced ensemble learner UBag-svm and UBst-rf achieve top performance with EA-Z. Conclusion: Our study indicates the effectiveness of reducing ranking errors for classifier-based effort-aware defect prediction. We recommend using EA-Z with imbalanced ensemble learning.

Thu 20 Jun

Displayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change

11:00 - 12:25
DefectsIndustry / Research Papers / Short Papers, Vision and Emerging Results / Journal-first at Room Capri
Chair(s): Davide Falessi University of Rome Tor Vergata, Italy
Context Switch Sensitive Fault LocalizationDistinguished Paper Award
Research Papers
Ferenc Horv�th University of Szeged, Department of Software Engineering, Roland Aszmann University of Szeged, Department of Software Engineering, Péter Attila Soha Department of Software Engineering, University of Szeged, Árpád Beszédes Department of Software Engineering, University of Szeged, Tibor Gyimothy
Improving classifier-based effort-aware software defect prediction by reducing ranking errors
Research Papers
Yuchen GUO Xi'an Jiaotong University, Martin Shepperd Brunel University London, Ning Li School of Computer Science, Northwestern Polytechnical University
Issues and Their Causes in WebAssembly Applications: An Empirical Study
Research Papers
Muhammad Waseem University of Jyväskylä, Jyväskylä, Finland, Teerath Das University of Jyväskylä, Aakash Ahmad School of Computing and Communications, Lancaster University Leipzig, Leipzig, Germany, Peng Liang Wuhan University, China, Tommi Mikkonen University of Jyvaskyla
Link to publication Pre-print Media Attached
Taming App Reliability: Mobile Analytics ‘in the wild’
Julian Harty Commercetest Limited, Arosha K Bandara The Open University
DOI File Attached
Improving the Quality of Software Issue Report Descriptions in Turkish: An Industrial Case Study at Softtech
Ethem Utku Aktas Softtech Inc., Ebru Cakmak Microsoft EMEA, Mete Cihad Inan Softtech Research and Development, Cemal Yilmaz Sabancı University
Unraveling the Influences on Bug Fixing Time: A Comparative Analysis of Causal Inference Model
Short Papers, Vision and Emerging Results
Sien Reeve O. Peralta Waseda University, Hironori Washizaki Waseda University, Yoshiaki Fukazawa Waseda University, Yuki Noyori Hitachi, Ltd., Shuhei Nojiri Hitachi, Ltd., Yokohama Reserch Laboratory, Hideyuki Kanuka Hitachi, Ltd.
File Attached