Deep learning(DL) is a branch of artificial intelligence that emulates human brain functions through computational processes. It has demonstrated its effectiveness across various domains; healthcare is no exception. Encouraging outcomes have been achieved in multiple healthcare applications, which include the classification of cancer, its prognosis, diagnosis, and classifying different molecular subtypes of cancer. Molecular subtyping using gene expression data may provide biological insights into cancer heterogeneity, which is instrumental in developing personalized medicine. The samples' scarcity relative to the high dimensional feature space remains a challenge in implementing deep learning models. This research investigates the effectiveness of clustering for reducing the dimensionality of the transcriptomic data and its subsequent influence on classification accuracy. The proposed method clusters the features and leverages the cluster centroids to train the classification model to predict the cancer molecular subtypes of colorectal cancer. The result comparison of the model with and without clustering reveals improved performance, in our proposed framework, while achieving parity with accuracy levels in others.

DevOps is a widely adopted practice to consistently develop and upgrade a system that is already in use. Between software development and operations, DevOps presupposes cross-functional cooperation and automation. The adoption and execution of DevOps in businesses are complicated since it necessitates adjustments to organizational, technical, and cultural factors. The implementation of DevOps in practice is thoroughly described in this systemic literature review (SLR). The study focuses on the identification of the manager's challenges in the DevOps environment and also intends to find the mitigation practices. In this article, SLR has been performed to identify the manager's challenges and the state-of-the-art mitigation strategies. This study identifies twenty challenges from the manager's perspective and the applied mitigation strategies to overcome the challenges. The findings of the current work would be beneficial in comprehending the DevOps idea, methods, and perceived impacts, particularly among managers while adopting DevOps in the organization.

DevOps is a software development approach that emphasize collaboration, communication and integration between development and operation teams to improve the speed and efficiency of software delivery. DevOps aims to automate and streamline the software development and deployment process. Nevertheless, when a software organization adopts DevOps, several challenges on infrastructure management, limited agility, scalability, increased cost, inconsistent environment, and security risks are faced. A solution is to adopt DevOps and Cloud together, but the integration requires advice because implementing new approaches for development and operations at the same time is also a challenge. The aim of this study is to identify and categorize success factors that positively influence the adoption of DevOps and Cloud in software organization and propose an integrated framework for factors of both dimensions. A systematic literature review (SLR) was conducted to collect the primary studies related to both fields for analysis. After the SLR, 40 success factors related to DevOps and Cloud are collected. These identified factors are further categorized into Technical, Organizational, and Social & Culture areas. The proposed framework can help practitioners and researchers to concentrate on the crucial areas that are essential for the successful adoption of DevOps and Cloud.

Abstract Cyber–Physical System (CPS) is the set of heterogeneous physical units linked to a network and performs complex operations to achieve a goal. Uncertainty increases with the increase in complexity of CPS. Thus, uncertainty needs to be mitigated to assure the quality and reliability of a CPS. This study aims to identify current state‐of‐the‐art approaches, tools, root causes, and metrics for uncertainty in the domain of CPS. We performed a systematic literature review and employed keyword‐based search on publisher sites to find potential studies. After applying the devised inclusion and exclusion criteria on identified potentially relevant studies, selection of studies is validated using an index engine. The core contributions of this study are (i) to categorize the tools used for uncertainty mitigation and existing root causes of uncertainty in CPS domain, (ii) to categorize the tools used for uncertainty mitigation and existing root causes of uncertainty in CPS domain, and (iii) to identify the state‐of‐the‐art methods that lack the ability to elaborate the metrics to measure the uncertainty in CPS. The results of the proposed study are beneficial in guiding future research on devising new approaches or tools to mitigate the causes of uncertainty in CPS.

Context: Software Engineering (SE) community has empirically investigated software defect prediction as a proxy to benchmark it as a process improvement activity to assure software quality. In the domain of software fault prediction, the performance of classification algorithms is highly provoked with the residual effects attributed to feature irrelevance and data redundancy issues. Problem: The meta-learning-based ensemble methods are usually carried out to mitigate these noise effects and boost the software fault prediction performance. However, there is a need to benchmark the performance of meta-learning ensemble methods (as fault predictor) to assure software quality control and aid developers in their decision making. Method: We conduct an empirical and comparative study to evaluate and benchmark the improvement in the fault prediction performance via meta-learning ensemble methods as compared to their component base-level fault predictors. In this study, we perform a series of experiments with four well-known meta-level ensemble methods Vote, StackingC (i.e., Stacking), MultiScheme, and Grading. We also use five high-performance fault predictors Logistic (i.e., Logistic Regression), J48 (i.e., Decision Tree), IBK (i.e. k-nearest neighbor), NaiveBayes, and Decision Table (DT). Subsequently, we performed these experiments on public defect datasets with k-fold (k=10) cross-validation. We used F-measure and ROC-AUC (Receiver Operating Characteristic-Area Under Curve) performance measures and applied the four non-parametric tests to benchmark the fault prediction performance results of meta-learning ensemble methods. Results and Conclusion: we conclude that meta-learning ensemble methods, especially Vote could outperform the base-level fault predictors to tackle the feature irrelevance and redundancy issues in the domain of software fault prediction. Having said that, their performance is highly related to the number of base-level classifiers and the set of software fault prediction metrics.

In large, collaborative open-source projects, developers must follow good coding standards to ensure the quality and sustainability of the resulting software. This is especially a challenge in high-performance computing projects, which admit a diverse set of contributions over decades of development. Some successful projects, such as the Portable, Extensible Toolkit for Scientific Computation (PETSc), have created comprehensive developer documentation, including specific code quality rules, which should be followed by contributors. However, none of the widely used and highly active open-source HPC projects have a way to automatically check whether these rules, typically expressed informally in English, are being violated. Hence, compliance checking is labor-intensive and difficult to ensure. To address this issue, we propose an automated method for detecting rule violations in HPC applications based on the PETSc development rules. In our empirical study, we consider 46 PETSc-based applications and assess the violations of two C-usage rules. The experimental results demonstrate the efficacy of the proposed method in identifying PETSc rule violations, which can be broadened to other HPC frameworks and extended by us and others in the community to include more rules.

The employment of design patterns is considered as a benchmark of software quality in terms of reducing the number of software faults. However, the quantification of the information about the hinder design issues such as the number of roles, type of design pattern, and their association with anti‐pattern classes is still required. The authors propose a new methodology to evaluate the impact of certain design issues on the software quality in terms of quantification of fault density. Firstly, they mine the required information about the classes of each system under study. Secondly, they describe taxonomy to group the classes. Subsequently, they used statistical techniques to formulate and benchmark the results. They include the analysis of four open source projects with six design patterns and six anti‐patterns in the case study. The main consequences are (i) the pattern participant classes are less dense in faults, (ii) the classes involved in the structural association between design patterns and anti‐patterns are denser in faults, (iii) the pattern participant classes with multi‐role and anti‐pattern smell association is denser in faults as compared to others. The significant difference between fault density distributions of groups of classes is still unclear and required further empirical investigation.

Abstract The phenomenon of global software development (GSD) has been adopted by a majority of the software development firms to achieve the significant benefits it offers. However, there are many challenges faced by the GSD organizations, which are mainly related to requirements change management (RCM). The key objective of this study is to identify the challenges of RCM process in GSD domain. The systematic literature review (SLR) approach has been used to investigate the challenges of RCM activities, and a total of 30 challenges were identified. We have further classified the identified challenges in the domain of client and vendor GSD organizations, aiming to provide a clear understanding of the RCM process and its challenges in the context of both types of GSD organizations. The identified challenges were also categorized into three core types according to the organization size (small, medium sized, or large), which highlights the significance of each challenge for a specific organizational size. In addition, the criticality of the identified challenges was assessed using the criteria of challenges having a frequency greater than or equal to 50%. According to the findings of this study, a framework is provided that could help GSD organizations address the problems related to RCM in a GSD environment.

Abstract Software development firms have begun adopting the practice of global software development (GSD). The main reason for the shift toward globalization is the various benefits received by software development firms. However, there are several issues faced by GSD organizations, particularly those associated with software process improvement (SPI). It has been noticed that a formal process improvement approach could assist in successfully executing development activities in GSD. The core objective of this research work is to develop a global software engineering process improvement model (GSEPIM) to assess and improve software process activities in a GSD environment. The proposed model will be developed based on existing models in other domains, an empirical study conducted with GSD practitioners, and an understanding of critical success factors and challenges of SPI. In this study, the first step in the development of GSEPIM is completed by identifying the challenges of SPI in GSD and presenting a solution in the form of a robust framework.

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.

The structural complexity of design components (e.g. Classes) is proportional to design quality at the system level and is quantified via the object-oriented metrics. The frequent use of design patterns causes of too much abstraction and can increase the structural complexity of design components. Though, in our previous work, we have empirically investigated the impact of use intensity of design pattern on the system level quality attributes. However, the empirical investigation of the effect of usage of design patterns on the design properties is still required. In this regard, we conduct an empirical study and perform a case study which includes the analysis 1) the existence of a correlation between design pattern usage and design metrics, 2) the confounding effect of system size (number of classes) on the correlation, and 3) how the change in number of employed design pattern instances affects the structural complexity in the subsequent releases of a system. The result of this study suggests that structural complexity associated with aggregation, coupling, functional abstraction design properties has a significant relationship with the employed instances of Template, Adapter-Command, Singleton, and Factory Method design patterns.

Context: In the plethora of studies, it has been empirically investigated that the incidence of design pattern instances can be considered as an indicator to elaborate the software design. The developers, who have more concern with design quality, are interested to know the effect of use intensity of design patterns on the system level design quality attributes. Goal: The objective of our study is to empirically investigate the effect of the frequent use of the Gang-of-Four (GoF) design patterns on the design quality attributes. Method: We perform a case study which includes three analyses in order to investigate, 1) the existence of a correlation between design pattern usage and design quality attributes, 2) the confounding effect of system size (number of classes) on the correlation, and 3) how the change in number of employed design pattern instances affects the design quality in the subsequent releases of a system. Results: The result of this study suggests that the reusability, flexibility and understandability have a significant relationship with the employed instances of Template, Adapter-Command, Singleton and State-Strategy design patterns, however, it is affected by the confounding effect of system size. Subsequently, in the subsequent releases of an open source project named velocity, we observed the use intensity of Singleton, Adapter-Command, and State-Strategy design patterns can improve the design quality in term of reusability and flexibility attributes.

Trend of software development is changing rapidly most of the software development organizations are trying to globalize their activities throughout the world. This trend leads towards a phenomenon called Global Software Development (GSD). The main reason behind the software globalization is its various benefits. Besides these benefits, software organizations are facing various challenges. One of these challenges is communication which is considered a big challenge in GSD and it becomes more complicated during the Requirements Change Management (RCM) process due to three factors, they are Geographical, Socio-cultural and Temporal distances. This paper presents a framework which shows the effect of these factors on communication during RCM process in GSD. Communication is the core function of collaboration which allows information to be exchanged between the team members. A pilot study has been conducted in three GSD organizations. A quantitative research method has been used to collect data. The findings from the survey data show that these three factors have a strong negative impact on communication process in GSD.