A regression test selection technique for embedded software

Abstract

The current approaches for regression test selection of embedded programs are usually based on dataand control-dependency analyses, often augmented with human reasoning. Existing techniques do not take into account additional execution dependencies which may exist among code elements in such programs due to features such as tasks, task deadlines, task precedences, and intertask communications. In this context, we propose a model-based regression test selection technique for such programs. Our technique first constructs a graph model of the program; the proposed graph model has been designed to capture several characteristics of embedded programs, such as task precedence order, priority, intertask communication, timers, exceptions and interrupt handlers, which we consider important for regression-test selection. Our regression test selection technique selects test cases based on an analysis of the constructed graph model. We have implemented our technique to realize a prototype tool. The experimental results obtained using this tool show that, on average, our approach selects about 28.33% more regression test cases than those selected by a traditional approach. We observed that, on average, 36.36% of the fault-revealing test cases were overlooked by the existing regression test selection technique. ACM Reference Format:Biswas, S., Mall, R., and Satpathy, M. 2013. A regression test selection technique for embedded software.

Optimizing Regression Testing with AHP-TOPSIS Metric System for Effective Technical Debt Evaluation

“…Many proposed testing approaches [12,35,36] are inadequate for identifying and quantifying TD. A new model was implemented to address TD in regression testing by selecting test cases whose omission may contribute to a high TD value, instead of choosing random test cases or those based on some form of single objective and expert judgments [42]. The proposed model is described in the following sequence.…”

Regression test selection using class, sequence and activity diagrams

“…It is suggested that for empirical studies concepts should be evaluated rather than the technical implementations. Biswas et al [8] reviewed various regression test selection techniques for different programming paradigms such as procedural, object oriented, aspect based, component based, database and web applications based. It is found that the techniques which use finer level granularity are more accurate than techniques which use a higher level of abstractions.…”