On the Effectiveness of Modular Testing in EvoSuite
This program is tentative and subject to change.
This paper explores the effectiveness of modular randomized testing for object oriented programs in Java. Modular testing involves testing individual components of a program in isolation. Often times, for effective test generation, a series of non-target setup calls must be included to obtain high coverage of the target component. In this work, we evaluate and improve modular testing with the EvoSuite test generator. We find that due to strict restrictions that disallow calls to non-target setup methods, EvoSuite’s modular testing mode is ineffective and often results in low branch coverage. We propose an enhancement to EvoSuite that relaxes this restriction, allowing non-target methods to be included in the test prefixes. This modification draws inspiration from developer-written fuzz drivers, which often invoke setup methods to properly initialize the state before testing the target method. To ensure meaningful test generation, we modify EvoSuite’s fitness function to focus branch coverage contributions on the call chain originating from the target method. Our approach is evaluated on a subset of the SF100 benchmark, showing a 15.15% improvement in coverage of the target methods.
This program is tentative and subject to change.
Tue 16 JunDisplayed time zone: Mountain Time (US & Canada) change
14:00 - 18:00 | |||
14:00 60mKeynote | Compositional Data-Flow Analysis at Industrial Scale SOAP Michael Emmi Amazon Web Services | ||
15:00 20mTalk | Scaling Static Code Analysis Adoption at WhatsApp iOS SOAP Ákos Hajdu Meta, Jorge Mendez Meta, Sander van Valkenburg Meta, Artem Kupriianets Meta, Matteo Marescotti Meta, Dulma Churchill Meta, Sopot Cela Meta | ||
15:50 60mKeynote | Compiler-assisted Translation Validation SOAP Qirun Zhang Georgia Institute of Technology | ||
16:50 20mTalk | On the Effectiveness of Modular Testing in EvoSuite SOAP Elizabeth Dinella Bryn Mawr College | ||
17:10 20mTalk | LLM-Integrated Declarative Program Analysis SOAP Sara Baradaran University of Southern California, Amirmohammad Nazari University of Southern California, Mukund Raghothaman University of Southern California | ||
17:30 20mTalk | Detecting Data Leaks in Multi-User LLM Apps via Automated User-Scoped Taint Analysis SOAP Sanjib Kumar Sen Texas A&M University - Corpus Christi, Bozhen Liu Texas A&M University - Corpus Christi | ||
17:50 10mDay closing | Closing Remarks SOAP | ||
