A number of studies in the literature have developed effective models to address prediction tasks related to a software product such as estimating its development effort, or its change/defect proneness. These predictions are critical as they help in identifying weak areas of a software product and thus guide software project managers in effective allocation of project resources to these weak parts. Such practices assure good quality software products. Recently, the use of search-based approaches (SBAs) for developing software prediction models (SPMs) has been successfully explored by a number of researchers. However, in order to develop effective and practical SPMs it is imperative to analyse various sources of threats. This study extensively reviews 93 primary studies, which use SBAs for developing SPMs of four commonly used software attributes (effort, defect-proneness, maintainability and change-proneness) in order to discuss and identify the various sources of threats while using these approaches for SPMs. The study also lists various actions that may be taken in order to minimise these threats. Furthermore, best practice examples in literature and the year-wise trends of threats indicating the most common threats missed by researchers are provided to help academicians and practitioners in designing effective studies for developing SPMs using SBAs.

The generation of multiple-path test cases can greatly enhance the efficiency of path-wise testing. Various methods adopting meta-heuristic algorithm to generate multiple-path test cases have been proposed, but existing methods focus on improving the meta-heuristic algorithm to get better test case generation efficiency, and test cases covering each path needs to be generated by meta-heuristic algorithm searching. To improve efficiency, a test case generation method for multiple-path coverage is proposed in this study, which combines a particle swarm optimisation (PSO) algorithm with metamorphic relations (MRs). The method first generates a test case using the PSO algorithm, and then generates new test cases by repeatedly using MRs between test cases. This method reduces evolving numbers of PSO algorithm. The experimental results show that the proposed method can significantly enhance the efficiency in terms of fitness evaluations and time consumption.

Software testing consumes a significant portion of software effort. Program entities such as branch or definition–use pairs (DUPs) are used in diverse software development tasks. In this study, the authors present a novel evolution-based approach to generating test data for all definition–use coverage. First, the subset of DUPs, which can ensure the coverage adequacy, is computed by a reduction algorithm for the whole DUPs. Then they apply a genetic algorithm to generate test data for the subset of DUPs. Furthermore, the fitness of an individual depends on the matching degree between the traversed path and the definition-clear path of each target DUP. They also investigate the coverage and the size of test cases of test data generation by applying the authors’ approach on 15 widely used subject programs. The experimental results show that their approach can reduce the size of test cases that generated without affecting the coverage rate.

Search-based software engineering involves the application of optimisation methods to solve software engineering problems. One of the most significant difficulties in testing software systems is the effort needed to build the test suites required to validate a software system, which efficiently exposes faults. Given the importance of the software testing stage, a specific sub-area known as search-based software testing has become relevant in recent years. In this work, a search-based software testing algorithm for constructing covering arrays is proposed. A covering array is a combinatorial structure that can be used as a set of test cases. By utilising this algorithm, the authors reduce the size of 893 test suites.

The majority of organisations are globalising their software development activities by following the ideas of global software development (GSD). The motivation behind the adoption of GSD phenomena are the list of benefits gained by the software industry. However, there are different challenges face by the GSD organisations, particularly the issues related to software process improvement (SPI). The aim of this study is the identification and classification into categories of the success factors that can impact SPI initiatives taken in GSD organisations. The systematic literature review (SLR) method has been used to extract the success factors from the literature. SLR phases, ‘planning, conducting, and reporting the review’ have been followed to perform this study. Totally, 15 success factors were identified and classified into the six main categories. The authors have also reported the critical success factors of SPI, i.e. management commitment, staff involvement, roles and responsibilities, communication, and resources allocation. This article also reported the similarities and differences between the success factors classified on the bases of client-vendor organisation and size of the organisation. The identified factors can contribute towards the implementation of SPI programme in both client and vendor GSD organisations because these factors represent key areas of process improvement.

Software defect prediction (SDP) technology is receiving widely attention and most of SDP models are trained on data from the same project. However, at an early phase of the software lifecycle, there are little to no within-project training data to learn an available supervised defect-prediction model. Thus, cross-project defect prediction (CPDP), which is learning a defect predictor for a target project by using labelled data from a source project, has shown promising value in SDP. To better perform the CPDP, most current studies focus on filtering instances or selecting features to weaken the impact of irrelevant cross-project data. Instead, the authors propose a novel multiple-components weights (MCWs) learning model to analyse the varying auxiliary power of multiple components in a source project to construct a more precise ensemble classifiers for a target project. By combining the MCW model with kernel mean matching algorithm, their proposed approach adjusts the source-instance weights and source-component weights to jointly alleviate the negative impacts of irrelevant cross-project data. They conducted comprehensive experiments by employing 15 real-world datasets to demonstrate the advantages and effectiveness of their proposed approach.

Development of software agents according to belief–desire–intention (BDI) model usually becomes challenging due to autonomy, distributedness, and openness of multi-agent systems (MAS). Hence, here, a domain-specific modelling language (DSML), called DSML4BDI, is introduced to support development of BDI agents. The syntax of the language provides the design of agent components required for the construction of the system according to the specifications of BDI architecture. The implementation of designed MAS on Jason BDI platform is also possible via model-to-text transformations built in the DSML. The comparative evaluation results showed that a significant amount of artefacts required for the exact MAS implementation can be automatically achieved by employing DSML4BDI. Moreover, time needed for developing a BDI agent system from scratch can be reduced to one-third in the case of using DSML4BDI. Finally, qualitative assessment, based on the developers’ feedback, exposed how DSML4BDI facilitates development of BDI agents.

Requirements elicitation is a critical activity that forms part of the requirements engineering process because it has to discover what the software must do through a solid understanding of the wishes and needs of the various stakeholders and to transform them into software requirements. However, in spite of its relevance, there are only a few systematic literature reviews that provide scientific evidence about the effectiveness of the techniques used to elicit software requirements. This study presents a systematic review of relevant literature on requirements elicitation techniques, from 1993 to 2015, by addressing two research questions: Which mature techniques are currently used for eliciting software requirements? and Which mature techniques improve the elicitation effectiveness? Prior literature assumes that such ‘maturity’ leads to a better-quality understanding of stakeholders’ desires and needs, and thus an increased likelihood that a resulting software will satisfy those requirements. This research paper found 140 studies to answer these questions. The findings describe which elicitation techniques are effective and in which situations they work best, taking into account the product which must be developed, the stakeholders’ characteristics, the type of information obtained, among other factors.