Software defect prediction is a promising approach aiming to improve software quality and testing efficiency by providing timely identification of defect-prone software modules before the actual testing process begins. These prediction results help software developers to effectively allocate their limited resources to the modules that are more prone to defects. In this paper, a hybrid heterogeneous ensemble approach is proposed for the purpose of software defect prediction. Heterogeneous ensembles consist of set of classifiers of different learning base methods in which each of them has its own strengths and weaknesses. The main idea of the proposed approach is to develop expert and robust heterogeneous classification models. Two versions of the proposed approach are developed and experimented. The first is based on simple classifiers, and the second is based on ensemble ones. For evaluation, 21 publicly available benchmark datasets are selected to conduct the experiments and benchmark the proposed approach. The evaluation results show the superiority of the ensemble version over other well-regarded basic and ensemble classifiers.

Software defect prediction (SDP) is crucial in the early stages of defect-free software development before testing operations take place. Effective SDP can help test managers locate defects and defect-prone software modules. This facilitates the allocation of limited software quality assurance resources optimally and economically. Feature selection (FS) is a complicated problem with a polynomial time complexity. For a dataset with N features, the complete search space has $2^N$ feature subsets, which means that the algorithm needs an exponential running time to traverse all these feature subsets. Swarm intelligence algorithms have shown impressive performance in mitigating the FS problem and reducing the running time. The moth flame optimization (MFO) algorithm is a well-known swarm intelligence algorithm that has been used widely and proven its capability in solving various optimization problems. An efficient binary variant of MFO (BMFO) is proposed in this paper by using the island BMFO (IsBMFO) model. IsBMFO divides the solutions in the population into a set of sub-populations named islands. Each island is treated independently using a variant of BMFO. To increase the diversification capability of the algorithm, a migration step is performed after a specific number of iterations to exchange the solutions between islands. Twenty-one public software datasets are used for evaluating the proposed method. The results of the experiments show that FS using IsBMFO improves the classification results. IsBMFO followed by support vector machine (SVM) classification is the best model for the SDP problem over other compared models, with an average G-mean of 78%.

Over the last few years, the arena of mobile application development has expanded considerably beyond the demand of the world's software markets. With the growing number of mobile software companies and the increasing sophistication of smartphone technology, developers have been establishing several categories of applications on dissimilar platforms. However, developers confront several challenges when undertaking mobile application projects. In particular, there is a lack of consolidated systems that can competently, promptly and efficiently provide developers with personalised services. Hence, it is essential to develop tailored systems that can recommend appropriate tools, IDEs, platforms, software components and other correlated artifacts to mobile application developers. This paper proposes a new recommender system framework comprising a robust set of techniques that are designed to provide mobile app developers with a specific platform where they can browse and search for personalised artifacts. In particular, the new recommender system framework comprises the following functions: (i) domain knowledge inference module: including various semantic web technologies and lightweight ontologies; (ii) profiling and preferencing: a new proposed time- aware multidimensional user modelling; (iii) query expansion: to improve and enhance the retrieved results by semantically augmenting users’ query; and (iv) recommendation and information filtration: to make use of the aforementioned components to provide personalised services to the designated users and to answer a user’s query with the minimum mismatches.

Introduction: Requirements traceability recovery is commonly evaluated as a single ranking task, although practical review settings differ in the evidence available before review, the inspection budget, and the possibility of analyst feedback. We present TraceRank, a configurable review-support framework that makes recovery assumptions explicit and enables only the recovery modes that are valid for the available evidence and review process. Methods: TraceRank separates cold-start recovery, partial-trace recovery, selective validation, fixed-budget review, and interactive feedback. It organizes retrieval, semantic-structural, dependency, hypergraph, validated-link, and feedback evidence within a common recovery pipeline. We evaluate TraceRank on five traceability datasets: eTour, iTrust, SMOS, eANCI, and LibEST. Results: In the cold-start setting, the best retained configuration recovers 404 of 501 true links at Top-30 (LinkRecall@30 = 80.6%) and completes 33 of 60 held-out requirements. Under the partial-trace assumption, the Validated Trace Responsibility Graph (VTRG) recovers 465 links (92.8%), completes 43 requirements, and reduces severe misses to 12. Across the same five requirement-level split seeds used for the split-robustness analysis, VTRG achieves mean LinkRecall@30 of 91.3% (SD 4.0%) and mean CompleteRate@30 of 76.5% (SD 8.1%). Archived fixed-budget and simulated-feedback outputs are retained only as exploratory review-process sensitivity evidence because the originating row-level priority queue and sequential update history are not preserved in the clean archive; they are therefore not treated as confirmatory evidence in the primary conclusions. Discussion: The results support a configuration-aware interpretation of trace recovery: cold-start, partial-trace, fixed-budget, and feedback-based recovery are different operating settings and should not be interpreted as one universal static ranking problem.

Over the last few years, the arena of mobile application development has expanded considerably beyond the balance of the world\'s software markets. With the growing number of mobile software companies, and the mounting sophistication of smartphones\' technology, developers have been building several categories of applications on dissimilar platforms. However, developers confront several challenges through the implementation of mobile application projects. In particular, there is a lack of consolidated systems that are competent to provide developers with personalised services promptly and efficiently. Hence, it is essential to develop tailored systems which can recommend appropriate tools, IDEs, platforms, software components and other correlated artifacts to mobile application developers. This paper proposes a new recommender system framework comprising a fortified set of techniques that are designed to provide mobile app developers with a distinctive platform to browse and search for the personalised artifacts. The proposed system make use of ontology and semantic web technology as well as machine learning techniques. In particular, the new RS framework comprises the following components; (i) domain knowledge inference module: including various semantic web technologies and lightweight ontologies; (ii) profiling and preferencing: a new proposed time-aware multidimensional user modelling; (iii) query expansion: to improve and enhance the retrieved results by semantically augmenting users\' query; and (iv) recommendation and information filtration: to make use of the aforementioned components to provide personalised services to the designated users and to answer a user\'s query with the minimum mismatches.