Time-Space Efficient Regression Testing for Configurable Systems

Configurable systems are those that can be adapted from a set of options. They are prevalent and testing them is important and challenging. Existing approaches for testing configurable systems are either unsound (i.e., they can miss fault-revealing configurations) or do not scale. This paper proposes EvoSPLat, a regression testing technique for configurable systems. EvoSPLat builds on our previously-developed technique, SPLat, which explores all dynamically reachable configurations from a test. EvoSPLat is tuned for two scenarios of use in regression testing: Regression Configuration Selection (RCS) and Regression Test Selection (RTS). EvoSPLat for RCS prunes configurations (not tests) that are not impacted by changes whereas EvoSPLat for RTS prunes tests (not configurations) which are not impacted by changes. Handling both scenarios in the context of evolution is important. Experimental results show that EvoSPLat is promising. We observed a substantial reduction in time (22%) and in the number of configurations (45%) for configurable Java programs. In a case study on a large real-world configurable system (GCC), EvoSPLat reduced 35% of the running time. Comparing EvoSPLat with sampling techniques, 2-wise was the most efficient technique, but it missed two bugs whereas EvoSPLat detected all bugs four times faster than 6-wise, on average.Comment: 14 page

Detecting Feature Requests of Third-Party Developers through Machine Learning: A Case Study of the SAP Community

The elicitation of requirements is central for the development of successful software products. While traditional requirement elicitation techniques such as user interviews are highly labor-intensive, data-driven elicitation techniques promise enhanced scalability through the exploitation of new data sources like app store reviews or social media posts. For enterprise software vendors, requirements elicitation remains challenging because app store reviews are scarce and vendors have no direct access to users. Against this background, we investigate whether enterprise software vendors can elicit requirements from their sponsored developer communities through data-driven techniques. Following the design science methodology, we collected data from the SAP Community and developed a supervised machine learning classifier, which automatically detects feature requests of third-party developers. Based on a manually labeled data set of 1,500 questions, our classifier reached a high accuracy of 0.819. Our findings reveal that supervised machine learning models are an effective means for the identification of feature requests