Software systems are now ubiquitous and are used every day for automation purposes in personal and enterprise applications; they are also essential to many safety-critical and mission-critical systems, e.g., air traffic control systems, autonomous cars, and SCADA systems. With the availability of massive storage capabilities, high speed Internet, and the advent of Internet of Things (IoT) devices, modern software systems are growing in both size and complexity. Maintaining a high quality of such complex systems while manually keeping the error rate at a minimum is a challenge. Therefore, automated detection of faulty components in a software system is important during software development and also post-delivery. Fault detection models usually needs to be trained on a labeled-balanced dataset with both faulty and non-faulty samples. Earlier work, e.g. Mohsin et al. (2016), showed that most real fault detection training dataset are imbalanced. Thereby, the trained model gets over-fitted and classifies faulty components as non-faulty components. The consequence of a high false negative rate is cumulative and results in generating more errors when using the model in other software systems – never seen before, which is very expensive. In this paper, we propose a software defect prediction ensemble model which considers the class imbalance problem in real software datasets. We use different oversampling techniques to build an ensemble classifier that can reduce the effect of low minority samples in the defective data. The proposed approach is verified using PROMISE software engineering dataset. The results show that our ensemble oversampling technique can more greatly reduce the false negative rate compared to the standard classification techniques and identify the faulty components more accurately resulting in a less expensive detection system (lowering the rate of non-faulty predictions of faulty modules).

Automated software defect prediction is an important and fundamental activity in the domain of software development. However, modern software systems are inherently large and complex with numerous correlated metrics that capture different aspects of the software components. This large number of correlated metrics makes building a software defect prediction model very complex. Thus, identifying and selecting a subset of metrics that enhance the software defect prediction method's performance are an important but challenging problem that has received little attention in the literature. The main objective of this paper is to identify significant software metrics, to build and evaluate an automated software defect prediction model. We propose two novel hybrid software defect prediction models to identify the significant attributes (metrics) using a combination of wrapper and filter techniques. The novelty of our approach is that it embeds the metric selection and training processes of software defect prediction as a single process while reducing the measurement overhead significantly. Different wrapper approaches were combined, including SVM and ANN, with a maximum relevance filter approach to find the significant metrics. A filter score was injected into the wrapper selection process in the proposed approaches to direct the search process efficiently to identify significant metrics. Experimental results with real defect-prone software data sets show that the proposed hybrid approaches achieve significantly compact metrics (i.e., selecting the most significant metrics) with high prediction accuracy compared with conventional wrapper or filter approaches. The performance of the proposed framework has also been verified using a statistical multivariate quality control process using multivariate exponentially weighted moving average. The proposed framework demonstrates that the hybrid heuristic can guide the metric selection process in a computationally efficient way by integrating the intrinsic characteristics from the filters into the wrapper and using the advantages of both the filter and wrapper approaches.

Software development outsourcing is becoming more and more famous because of the advantages like cost abatement, process enhancement, and coping with the scarcity of needed resources. Studies confirm that unfortunately a large proportion of the software development outsourcing projects fails to realize anticipated benefits. Investigations into the failures of such projects divulge that in several cases software development outsourcing projects are failed because of the issues that are associated with requirements engineering process. The objective of this study is the identification and the ranking of the commonly occurring issues of the requirements engineering process in the case of software development outsourcing. For this purpose, contemporary literature has been assessed rigorously, issues faced by practitioners have been identified and three questionnaire surveys have been organized by involving experienced software development outsourcing practitioners. The Delphi technique, cut-off value method and 50% rule have also been employed. The study explores 150 issues (129 issues from literature and 21 from industry) of requirements engineering process for software development outsourcing, groups the 150 issues into 7 identified categories and then extricates 43 customarily or commonly arising issues from the 150 issues. Founded on ‘frequency of occurrence’ the 43 customarily arising issues have been ranked with respect to respective categories (category-wise ranking) and with respect to all the categories (overall ranking). Categories of the customarily arising issues have also been ranked. The issues’ identification and ranking contribute to design proactive software project management plan for dealing with software development outsourcing failures and attaining conjectured benefits of the software development outsourcing.