Improving Impact Analysis and Regression Testing Using Field Data

Abstract

Software products are often released with missing functionality, errors, or incompatibilities that may result in failures, inferior performances, or, more generally, user dissatisfaction. In previous work, we presented the Gamma approach, which enables analyses that (1) rely on actualeld data instead of synthetic in-house data and (2) leverage the vast and heterogeneous resources of an entire user community. In this paper, we investigate the use of the Gamma approach to support and improve two fundamental tasks performed by software engineers during maintenance: impact analysis and regression testing. We propose a new approach that leverages eld data to perform these two tasks. We also discuss ongoing empirical studies, performed on a real subject and on a real user population, to assess the feasibility of the approach.


Related research categories:
(1) Impact Analysis
(2) Deployed Software
(3) Monitoring
(4) Regression Testing

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