Adapting an Alarm Repositioning Algorithm to Data Races
Karoliine Holter
University of Tartu, 2023
Supervisors: Vesal Vojdani, Simmo Saan
This master’s thesis addresses the challenge of enhancing the usability of sound static analyzers, specifically focusing on the state-of-the-art data race verifier Goblint. The aim is to soundly post-process the warnings generated by Goblint to make them more understandable for developers, thereby increasing the adoption of sound analyzers in practice. The thesis adapts and extends the warning repositioning algorithm of Muske et al. for data race warnings in multi-threaded C programs. Contributions include identifying and implementing a potential solution within the Goblint analyzer, extending the method for data races, and evaluating and analyzing the adapted algorithm in terms of the reduced distance between possible causes and warnings, as well as the impact on the quality of data race warnings.