Important Task In Software Engineering Research Articles

Classification with reject option for software defect prediction

ContextSoftware defect prediction (SDP) is an important task in software engineering. Along with estimating the number of defects remaining in software systems and discovering defect associations, classifying the defect-proneness of software modules plays an important role in software defect prediction. Several machine-learning methods have been applied to handle the defect-proneness of software modules as a classification problem. This type of “yes” or “no” decision is an important drawback in the decision-making process and if not precise may lead to misclassifications. To the best of our knowledge, existing approaches rely on fully automated module classification and do not provide a way to incorporate extra knowledge during the classification process. This knowledge can be helpful in avoiding misclassifications in cases where system modules cannot be classified in a reliable way. ObjectiveWe seek to develop a SDP method that (i) incorporates a reject option in the classifier to improve the reliability in the decision-making process; and (ii) makes it possible postpone the final decision related to rejected modules for an expert analysis or even for another classifier using extra domain knowledge. MethodWe develop a SDP method called rejoELM and its variant, IrejoELM. Both methods were built upon the weighted extreme learning machine (ELM) with reject option that makes it possible postpone the final decision of non-classified modules, the rejected ones, to another moment. While rejoELM aims to maximize the accuracy for a rejection rate, IrejoELM maximizes the F-measure. Hence, IrejoELM becomes an alternative for classification with reject option for imbalanced datasets. ResultsrejoEM and IrejoELM are tested on five datasets of source code metrics extracted from real world open-source software projects. Results indicate that rejoELM has an accuracy for several rejection rates that is comparable to some state-of-the-art classifiers with reject option. Although IrejoELM shows lower accuracies for several rejection rates, it clearly outperforms all other methods when the F-measure is used as a performance metric. ConclusionIt is concluded that rejoELM is a valid alternative for classification with reject option problems when classes are nearly equally represented. On the other hand, IrejoELM is shown to be the best alternative for classification with reject option on imbalanced datasets. Since SDP problems are usually characterized as imbalanced learning problems, the use of IrejoELM is recommended.

Dynamic program analysis—Reconciling developer productivity and tool performance

Dynamic program analysis tools serve many important software engineering tasks such as profiling, debugging, testing, program comprehension, and reverse engineering. Many dynamic analysis tools rely on program instrumentation and are implemented using low-level instrumentation libraries, resulting in tedious and error-prone tool development. Targeting this issue, we have created the Domain-Specific Language for Instrumentation (DiSL), which offers high-level programming abstractions especially designed for instrumentation-based dynamic analysis. When designing DiSL, our goal was to boost the productivity of tool developers targeting the Java Virtual Machine, without impairing the performance of the resulting tools. In this paper we assess whether DiSL meets this goal. First, we perform a controlled experiment to measure tool development time and correctness of the developed tools, comparing DiSL with a prevailing, state-of-the-art instrumentation library. Second, we recast 10 open-source software engineering tools in DiSL and compare source code metrics and performance with the original implementations. Our studies show that DiSL significantly improves developer productivity, enables concise tool implementations, and does not have any negative impact on tool performance.

SUPPORTING DECISION-MAKING IN SOFTWARE ENGINEERING WITH PROCESS SIMULATION AND EMPIRICAL STUDIES

Decision-making is a complex and important task in software engineering. The current state-of-the-practice is rather non-systematic as it typically relies upon personal judgment and experience without using explicit models. Empirical studies can help but they are costly to conduct and, to some extent, context dependent. Typically it is not efficient or even possible to conduct empirical studies for a large number of context parameter variations. Process simulation offers decision support as well, but currently suffers from a lack of empirical knowledge on the determinants of underlying system dynamics. In this paper we present an assessment of empirical knowledge and simulation techniques for the area of quality assurance planning. There is a strong interdependency between process simulation and empirical models for decision-making in this area: (a) profound empirical knowledge enables process simulation to support decision-making, and (b) the analysis of simulation results can point out situations and factors for which conducting empirical studies would be most worthwhile. This paper discusses critically some of the most important challenges for decision-making in the area of quality assurance planning.