Semantics-aware trace analysis

  • Authors:

  • Kevin J. Hoffman;Patrick Eugster;Suresh Jagannathan

  • Affiliations:

  • Purdue University, West Lafayette, IN, USA;Purdue University, West Lafayette, IN, USA;Purdue University, West Lafayette, IN, USA

  • Venue:
  • Proceedings of the 2009 ACM SIGPLAN conference on Programming language design and implementation
  • Year:
  • 2009

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Abstract

As computer systems continue to become more powerful and complex, so do programs. High-level abstractions introduced to deal with complexity in large programs, while simplifying human reasoning, can often obfuscate salient program properties gleaned from automated source-level analysis through subtle (often non-local) interactions. Consequently, understanding the effects of program changes and whether these changes violate intended protocols become difficult to infer. Refactorings, and feature additions, modifications, or removals can introduce hard-to-catch bugs that often go undetected until many revisions later. To address these issues, this paper presents a novel dynamic program analysis that builds a semantic view of program executions. These views reflect program abstractions and aspects; however, views are not simply projections of execution traces, but are linked to each other to capture semantic interactions among abstractions at different levels of granularity in a scalable manner. We describe our approach in the context of Java and demonstrate its utility to improve regression analysis. We first formalize a subset of Java and a grammar for traces generated at program execution. We then introduce several types of views used to analyze regression bugs along with a novel, scalable technique for semantic differencing of traces from different versions of the same program. Benchmark results on large open-source Java programs demonstrate that semantic-aware trace differencing can identify precise and useful details about the underlying cause for a regression, even in programs that use reflection, multithreading, or dynamic code generation, features that typically confound other analysis techniques.