Adaptive Regression Testing (ART) Strategies

Project Description

As software systems evolve, the types of maintenance activities that are applied to them change. Differences in versions can involve different amounts and types of code modifications, and this can affect the costs and benefits of regression testing techniques in different ways. It follows that across system lifetimes, there may be no single regression testing technique that is the most cost-effective technique to use on every version. To date, many regression testing techniques have been proposed, but only a little research has been done on the problem of helping practitioners choose appropriate techniques under particular system and process constraints. Further, no research has considered strategies for automatically selecting techniques to use on new versions as systems evolve.

This project addresses this by creating and empirically studying adaptive regression testing (ART) strategies. ART strategies are approaches that operate across system lifetimes, and attempt to identify the regression testing techniques that will be the most cost-effective for each regression testing session. In particular, we investigate Analytical Hierarchy Process (AHP) methods; these have been used in multiple criteria decision making processes on complex problems in areas such as agriculture, civil engineering, and software engineering but no research has addressed the possibility of incorporating AHP methods into regression testing strategies.

  • PI: Hyunsook Do
  • Graduate Researcher: Md. Junaid Arafeen and Md. Hossain