The prediction of whether a software change is fault-inducing or not in the software system using various learning methods, the study concerned in Just-In-Time Software Fault Prediction (JIT-SFP). Building such predicting model requires adequate training data. However, there needs to be more training data at the beginning of the software system. Cross-Project (CP) setting can subjugate this challenge by employing data from different software projects. It can achieve similar predictive performance to Within-Project (WP) fault prediction. It is still being determined to what level the CP training data can be useful in such a situation. Furthermore, it also needs to be discovered whether CP data are helpful in the initial phase of fault detection, and when there is an inadequate WP train set, CP could be beneficial to extend. This article deals with such investigations in real software projects. We proposed a new method by levering a deep belief network and long short-term memory called JITCP-Predictor. Out of ten, the proposed model significantly outperforms every ten project benchmark methods, and it is superior from 10.63% to 136.36% and 7.04% to 35.71% in terms of MCC and F-Measure, respectively. The mean values of MCC and F-Measure produced by JITCP-Predictor are 0.52 ± 0.021 and 0.76 ± 0.76, respectively. We also found that the proposed model is more suitable for large and moderate-size projects. The proposed model avoids class imbalance and overfitting problems and takes reasonable training costs.

All the known Group Decision Making (GDM) models assume the continuous availability of all decision-makers (DMs) during the Consensus Reaching Process (CRP). Factually, the constant presence of a DM means that the concerned DMs are interested in adequately contributing to the decision-making process, and the technical support continuously enables their support. However, in a realistic situation, one or more DMs may be unavailable at times in CRP iterations due to technical or non-technical reasons. This paper considers such a scenario wherein the DMs are sparsely present. Hence, working out a model to take care of such pertinent absences that eventually make GDM possible. The bounded confidence of an individual DM is used to facilitate CRP in evaluating the opinions of the unavailable DMs. We propose to assign weight to a DM based on their cumulative presence in the decision process. Consideration of the opinion of a DM in a particular iteration based on the opinion in the previous iterations in case of the absence of the concerned DM in an implementation shown here is shown to be helpful.

In blockchain-based systems, participants can be malicious. Therefore, this work first characterizes several properties expected in systems where the honest behaviour of involved parties plays significant role in the success. Considering these properties, a new consensus mechanism, Proof of Karma (PoK), is proposed based on karma (actions) of nodes. PoK incorporates a self-stabilizing leader election algorithm based on karma score to ensure consistency in the system. In PoK, both new and existing nodes get a fair chance to earn profit by becoming a leader and adding a valid block to the blockchain. PoK gives incentives and imposes penalties to encourage and discourage the nodes’ honest and malicious actions, respectively. PoK is analyzed with respect to the CAP theorem. The work provides security analysis to demonstrate the resistance of PoK against various blockchain specific attacks and karma specific attacks. Several experiments are also performed to assess the performance of PoK and compare it with the baseline model. The results show the feasibility, effectiveness, usability and scalability of PoK. PoK is also compared based on the characterized properties with various existing consensus mechanisms that consider malicious actions of nodes. PoK achieves consensus finality, decentralization and fairness, outperforming existing works.

Predicting defects during software testing reduces an enormous amount of testing effort and help to deliver a high‐quality software system. Owing to the skewed distribution of public datasets, software defect prediction (SDP) suffers from the class imbalance problem, which leads to unsatisfactory results. Overfitting is also one of the biggest challenges for SDP. In this study, the authors performed an empirical study of these two problems and investigated their probable solution. They have conducted 4840 experiments over five different classifiers using eight NASA projects and 14 PROMISE repository datasets. They suggested and investigated the varying kernel function of an extreme learning machine (ELM) along with kernel principal component analysis (K‐PCA) and found better results compared with other classical SDP models. They used the synthetic minority oversampling technique as a sampling method to address class imbalance problems and k‐fold cross‐validation to avoid the overfitting problem. They found ELM‐based SDP has a high receiver operating characteristic curve over 11 out of 22 datasets. The proposed model has higher precision and F ‐score values over ten and nine, respectively, compared with other state‐of‐the‐art models. The Mathews correlation coefficient (MCC) of 17 datasets of the proposed model surpasses other classical models' MCC.

Summary In multiprocessor environments, the scheduling algorithms play a significant role in maximizing system performance. In this paper, we propose a clustering‐based task scheduling algorithm called Edge Priority Scheduling (EPS) for multiprocessor environments. The proposed algorithm extends the idea of edge zeroing heuristic and uses the concept of edge priority to minimize the makespan of the task graph. The complexity of the EPS algorithm is O(|V||E|(|V| + |E|)), where |E| represents the number of edges and |V| denotes the number of nodes in the task graph. The experiments are performed for random task graphs and the task graphs generated from some representative real‐world applications such as Gaussian Elimination and Fast Fourier Transform. The performance of the EPS algorithm is compared with six well‐known algorithms such as EZ (Edge Zeroing), LC (Linear Clustering), CPPS (Cluster Pair Priority Scheduling), DCCL (Dynamic Computation Communication Load), RDCC (Randomized Dynamic Computation Communication), and LOCAL. The results show that the EPS algorithm outperforms the compared algorithms in terms of the normalized schedule length and speedup.

The Capability Maturity Model (proposed by SEI-CMU) does not consider component based software engineering (CBSE) principles in its considerations of levels and KPAs. It is therefore necessary to consider a model that is based on peculiarities and importance of CBSE and hence a new model under the name ICMM (Integrated Component Maturity Model) for this purpose is being proposed herein. The model, ICMM, is applicable for many types of organizations like organizations that develop components only, organizations that develop CBS or organizations that develop components along with CBS. This work starts a discussion and calls for more extensive research oriented studies by professionals and academicians for perfection of the model.

Summary form only given. Most of the task allocation models & algorithms in distributed computing system (DCS) require a priori knowledge of its execution time on the processing nodes. Since the task assignment is not known in advance, this time is quite difficult to estimate. We propose a cluster-based dynamic allocation scheme, in a distributed computing system, which eliminate this time requirement. Further, as opposed to a single task allocation, generally proposed in most of the models, we consider multiple tasks. A fuzzy function is used for both the module clustering and processor clustering. Dynamic invocation of clustering and assignment is considered. Experimental results show the efficacy of the proposed model.