Precisely Detecting Runtime Change Interactions for Evolving Software


Developers often make multiple changes to software. These changes are introduced to work cooperatively or to accomplish separate goals. However, changes might not interact as expected or may produce undesired side effects. Thus, it is crucial for software-development tasks to know exactly which changes interact. For example, testers need this information to ensure that regression test suites test the combined behaviors of changes. For another example, teams of developers must determine whether it is safe to merge variants of a program modified in parallel. Existing techniques can be used to detect at runtime potential interactions among changes, but these reports tend to be coarse and imprecise. To address this problem, in this paper, we first present a formal model of change interactions at the code level, and then describe a new technique, based on this model, for detecting at runtime such interactions with accuracy. We also present the results of a comparison of our technique with other techniques on a set of Java subjects. Our results clearly suggest that existing techniques are too inaccurate and only our technique, of all those studied, provides acceptable confidence in detecting real change interactions occurring at runtime.

Related research categories:
(1) Program Analysis
(2) Regression Testing
(3) Impact Analysis

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