Software metrics for green parallel computing of big data systems

Big Data is typically organized around a distributed file system on top of which the parallel algorithms can be executed for realizing the Big Data analytics. In general, the parallel algorithms can be mapped in different alternative ways to the computing platform. Hereby each alternative will perform differently with respect to the environmentally relevant parameters such as energy and power consumption. Existing studies on deployment of parallel computing algorithms have mainly focused on addressing general computing metrics such as speedup with respect to serial computing and efficiency of the use of the computing nodes. In this paper, we report on the elicitation of green metrics for big data systems that are required when analyzing deployment alternatives. To this end we use the existing systematic literature reviews and identify, and discuss the important green computing metrics for big data systems.</p

Model-based testing for software safety : a systematic mapping study

Testing safety-critical systems is crucial since a failure or malfunction may result in death or serious injuries to people, equipment, or environment. An important challenge in testing is the derivation of test cases that can identify the potential faults. Model-based testing adopts models of a system under test and/or its environment to derive test artifacts. This paper aims to provide a systematic mapping study to identify, analyze, and describe the state-of-the-art advances in model-based testing for software safety. The systematic mapping study is conducted as a multi-phase study selection process using the published literature in major software engineering journals and conference proceedings. We reviewed 751 papers and 36 of them have been selected as primary studies to answer our research questions. Based on the analysis of the data extraction process, we discuss the primary trends and approaches and present the identified obstacles. This study shows that model-based testing can provide important benefits for software safety testing. Several solution directions have been identified, but further research is critical for reliable model-based testing approach for safety.</p