Context: Graphical user interface (GUI) testing of mobile applications (apps) is significant from a user perspective to ensure that the apps are visually appealing and user‐friendly. Pattern‐based GUI testing (PBGT) is an innovative model‐based testing (MBT) approach designed to enhance user satisfaction and reusability while minimizing the effort required to model and test UIs of mobile apps. In the literature, several primary studies have been conducted in the domain of PBGT. Problem: The current state‐of‐the‐art lacks comprehensive secondary studies within the PBGT domain. To our knowledge, this area has insufficient focus on in‐depth research. Consequently, numerous challenges and limitations persist in the existing literature. Objective: This study aims to fill the gaps mentioned above in the existing body of knowledge. We highlight popular research topics and analyze their relationships. We explore current state‐of‐the‐art approaches and techniques, a taxonomy of tools and modeling languages, a list of reported UI test patterns (UITPs), and a taxonomy of writing UITPs. We also highlight practical challenges, limitations, and gaps in the targeted research area. Furthermore, the current study intends to highlight future research directions in this domain. Method: We conducted a systematic literature review (SLR) on PBGT in the context of Android and web apps. A hybrid methodology that combines the Kitchenham and PRISMA guidelines is adopted to achieve the targeted research objectives (ROs). We perform a keyword‐based search on well‐known databases and select 30 (out of 557) studies. Results: The current study identifies 11 tools used in PBGT and devises a taxonomy to categorize these tools. A taxonomy for writing UITPs has also been developed. In addition, we outline the limitations of the targeted research domain and future directions. Conclusion: This study benefits the community and readers by better understanding the targeted research area. A comprehensive knowledge of existing tools, techniques, and methodologies is helpful for practitioners. Moreover, the identified limitations, gaps, emerging trends, and future research directions will benefit researchers who intend to work further in future research.

Abstract Learning classifiers with uneven class distribution datasets poses a significant challenge in software defect prediction. This problem arises when the number of samples representing one class is significantly smaller than the others, leading to weak classification performance, particularly for minority class instances. Traditional classification models assuming equal class instances can result in low prediction accuracy and decision-making precision for minority class instances, raising concerns about identifying such instances accurately. To overcome this issue, this research proposes a hybrid technique that combines the Adaptive Synthetic Sampling (ADASYN) approach with a batch generator named the HADAB technique. ADASYN generates synthetic samples for the minority class, balancing the dataset and improving prediction accuracy. Conversely, the batch generator feeds data to the model in batches, enhancing training efficiency. The Multi-Layer Perceptron (MLP) serves as the base classifier in this study. The proposed HADAB technique significantly improves prediction accuracy and training efficiency without requiring additional parameter tuning, algorithm modification, or increasing complexity. We validate the performance of HADAB using publicly available NASA datasets encompassing diverse types. The results demonstrate the superiority of HADAB over traditional prediction accuracy methods. In conclusion, the proposed HADAB technique offers a practical and effective solution for handling class imbalance in software defect prediction, leading to improved prediction accuracy.

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 Regression testing remains a promising research area for the last few decades. It is a type of testing that aims at ensuring that recent modifications have not adversely affected the software product. After the introduction of a new change in the system under test, the number of test cases significantly increases to handle the modification. Consequently, it becomes prohibitively expensive to execute all of the generated test cases within the allocated testing time and budget. To address this situation, the test suite reduction (TSR) technique is widely used that focusses on finding a representative test suite without compromising its effectiveness such as fault‐detection capability. In this work, a systematic review study is conducted that intends to provide an unbiased viewpoint about TSR based on various types of search algorithms. The study's main objective is to examine and classify the current state‐of‐the‐art approaches used in search‐based TSR contexts. To achieve this, a systematic review protocol is adopted and, the most relevant primary studies (57 out of 210) published between 2007 and 2022 are selected. Existing search‐based TSR approaches are classified into five main categories, including evolutionary‐based, swarm intelligence‐based, human‐based, physics‐based, and hybrid, grounded on the type of employed search algorithm. Moreover, the current work reports the parameter settings according to their category, the type of considered operator(s), and the probabilistic rate that significantly impacts on the quality of the obtained solution. Furthermore, this study describes the comparison baseline techniques that support the empirical comparison regarding the cost‐effectiveness of a search‐based TSR approach. Finally, it isconcluded that search‐based TSR has great potential to optimally solve the TSR problem. In this regard, several potential research directions are outlined as useful for future researchers interested in conducting research in the TSR domain.

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.

Abstract Software defect prediction is the process of identifying defective files and modules that need rigorous testing. In the literature, several secondary studies including systematic reviews, mapping studies, and review studies have been reported. However, no research work such as a tertiary study that combines secondary studies has focused on providing a landscape of software defect prediction useful to understand the body of knowledge. Motivated by this, we intend to perform a tertiary study by following a systematic literature review protocol to provide a research landscape of the targeted domain. We synthesize the quality of the secondary studies and investigate the employed techniques and the performance evaluation measures for evaluating the software defect prediction model. Furthermore, this study aims at exploring different datasets employed in the reported experimentation. Moreover, the current study intends at highlighting the research trends, gaps, and opportunities in the targeted research domain. The results indicate that none of the reported defect prediction techniques can be regarded as the best; however, the reported techniques performed better in different testing situations. In addition, machine learning (ML)‐based techniques perform better than traditional statistical techniques mainly due to the potential of discovering the defects and generating generalized results. Moreover, the obtained results highlight the need for further work in the domain of ML‐based techniques. Furthermore, publicly available datasets should be considered for experimentation or replication purposes. The potential future work can focus on data quality, ethical ML, cross‐project defect prediction, early defect prediction process, class imbalance problem, and model overfitting.

Scientific Workflow Applications (SWFA) play a vital role for both service consumers and service providers in designing and implementing large and complex scientific processes. Previously, researchers used parallel and distributed computing technologies, such as utility and grid computing to execute the SWFAs, these technologies provide limited utilization for the shared resources. In contrast, the scalability and flexibility challenges are better handled by using cloud-computing technologies for SWFA. Since cloud computing offers a technology that can significantly utilize the amounts of storage space and computing resources necessary for processing large-size and complex SWFAs. The workflow pattern design has provided the facility of re-using previously developed workflow solutions that enable the developers to adopt them for the considered SWFA. Inspired by this, the researchers have adopted several patterns of design to better design the SWFA. Effective pattern design that can consider challenges that may not become visible only in the implementation stage of a SWFA. However, the selection of the most effective pattern design in accordance with an execution method, data size, and problem complexity of a SWFA remains a challenging task. Motivated by this, we have proposed a conceptual framework that facilitates in recommending a suitable pattern design based on the quality requirements and capabilities are given and advertised by cloud consumers and providers, respectively. Finally, guidelines to assist in a smooth migrating of SWFA from other computation paradigms to cloud computing.

The standard software development life cycle heavily depends on requirements elicited from stakeholders. Based on those requirements, software development is planned and managed from its inception phase to closure. Due to time and resource constraints, it is imperative to identify the high-priority requirements that need to be considered first during the software development process. Moreover, existing prioritization frameworks lack a store of historical data useful for selecting the most suitable prioritization technique of any similar project domain. In this paper, we propose a framework for prioritization of software requirements, called RePizer, to be used in conjunction with a selected prioritization technique to rank software requirements based on defined criteria such as implementation cost. RePizer assists requirements engineers in a decision-making process by retrieving historical data from a requirements repository. RePizer also provides a panoramic view of the entire project to ensure the judicious use of software development resources. We compared the performance of RePizer in terms of expected accuracy and ease of use while separately adopting two different prioritization techniques, planning game (PG) and analytical hierarchy process (AHP). The results showed that RePizer performed better when used in conjunction with the PG technique.