This program is tentative and subject to change.

Tue 16 Jun 2026 16:50 - 17:10 at Flatirons 4 - SOAP 2

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 Jun

Displayed time zone: Mountain Time (US & Canada) change

14:00 - 18:00
SOAP 2SOAP at Flatirons 4
14:00
60m
Keynote
Compositional Data-Flow Analysis at Industrial Scale
SOAP
Michael Emmi Amazon Web Services
15:00
20m
Talk
Scaling Static Code Analysis Adoption at WhatsApp iOS
SOAP
15:50
60m
Keynote
Compiler-assisted Translation Validation
SOAP
Qirun Zhang Georgia Institute of Technology
16:50
20m
Talk
On the Effectiveness of Modular Testing in EvoSuite
SOAP
Elizabeth Dinella Bryn Mawr College
17:10
20m
Talk
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
20m
Talk
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
10m
Day closing
Closing Remarks
SOAP