Browsing by Author "Catal, Cagatay"
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Article Citation Count: 3Deep Learning-Based Defect Prediction for Mobile Applications(Mdpi, 2022) Mıshra, Alok; Akbulut, Akhan; Catal, Cagatay; Mishra, Alok; Software EngineeringSmartphones have enabled the widespread use of mobile applications. However, there are unrecognized defects of mobile applications that can affect businesses due to a negative user experience. To avoid this, the defects of applications should be detected and removed before release. This study aims to develop a defect prediction model for mobile applications. We performed cross-project and within-project experiments and also used deep learning algorithms, such as convolutional neural networks (CNN) and long short term memory (LSTM) to develop a defect prediction model for Android-based applications. Based on our within-project experimental results, the CNN-based model provides the best performance for mobile application defect prediction with a 0.933 average area under ROC curve (AUC) value. For cross-project mobile application defect prediction, there is still room for improvement when deep learning algorithms are preferred.Article Citation Count: 41Empirical analysis of change metrics for software fault prediction(Pergamon-elsevier Science Ltd, 2018) Mıshra, Alok; Kumar, Sandeep; Kumar, Kuldeep; Mishra, Alok; Catal, Cagatay; Software EngineeringA quality assurance activity, known as software fault prediction, can reduce development costs arid improve software quality. The objective of this study is to investigate change metrics in conjunction with code metrics to improve the performance of fault prediction models. Experimental studies are performed on different versions of Eclipse projects and change metrics are extracted from the GIT repositories. In addition to the existing change metrics, several new change metrics are defined and collected from the Eclipse project repository. Machine learning algorithms are applied in conjunction with the change and source code metrics to build fault prediction models. The classification model with new change metrics performs better than the models using existing change metrics. In this work, the experimental results demonstrate that change metrics have a positive impact on the performance of fault prediction models, and high-performance models can be built with several change metrics. (C) 2018 Elsevier Ltd. All rights reserved.Review Citation Count: 34Hybrid Blockchain Platforms for the Internet of Things (IoT): A Systematic Literature Review(Mdpi, 2022) Mıshra, Alok; Catal, Cagatay; Kar, Gorkem; Mishra, Alok; Software EngineeringIn recent years, research into blockchain technology and the Internet of Things (IoT) has grown rapidly due to an increase in media coverage. Many different blockchain applications and platforms have been developed for different purposes, such as food safety monitoring, cryptocurrency exchange, and secure medical data sharing. However, blockchain platforms cannot store all the generated data. Therefore, they are supported with data warehouses, which in turn is called a hybrid blockchain platform. While several systems have been developed based on this idea, a current state-of-the-art systematic overview on the use of hybrid blockchain platforms is lacking. Therefore, a systematic literature review (SLR) study has been carried out by us to investigate the motivations for adopting them, the domains at which they were used, the adopted technologies that made this integration effective, and, finally, the challenges and possible solutions. This study shows that security, transparency, and efficiency are the top three motivations for adopting these platforms. The energy, agriculture, health, construction, manufacturing, and supply chain domains are the top domains. The most adopted technologies are cloud computing, fog computing, telecommunications, and edge computing. While there are several benefits of using hybrid blockchains, there are also several challenges reported in this study.Article Citation Count: 5Stress Detection Using Experience Sampling: A Systematic Mapping Study(Mdpi, 2022) Mıshra, Alok; Akbulut, Fatma Patlar; Catal, Cagatay; Mishra, Alok; Software EngineeringStress has been designated the "Health Epidemic of the 21st Century" by the World Health Organization and negatively affects the quality of individuals' lives by detracting most body systems. In today's world, different methods are used to track and measure various types of stress. Among these techniques, experience sampling is a unique method for studying everyday stress, which can affect employees' performance and even their health by threatening them emotionally and physically. The main advantage of experience sampling is that evaluating instantaneous experiences causes less memory bias than traditional retroactive measures. Further, it allows the exploration of temporal relationships in subjective experiences. The objective of this paper is to structure, analyze, and characterize the state of the art of available literature in the field of surveillance of work stress via the experience sampling method. We used the formal research methodology of systematic mapping to conduct a breadth-first review. We found 358 papers between 2010 and 2021 that are classified with respect to focus, research type, and contribution type. The resulting research landscape summarizes the opportunities and challenges of utilizing the experience sampling method on stress detection for practitioners and academics.Article Citation Count: 5Techniques for Calculating Software Product Metrics Threshold Values: A Systematic Mapping Study(Mdpi, 2021) Mıshra, Alok; Shatnawi, Raed; Catal, Cagatay; Akbulut, Akhan; Software EngineeringSeveral aspects of software product quality can be assessed and measured using product metrics. Without software metric threshold values, it is difficult to evaluate different aspects of quality. To this end, the interest in research studies that focus on identifying and deriving threshold values is growing, given the advantage of applying software metric threshold values to evaluate various software projects during their software development life cycle phases. The aim of this paper is to systematically investigate research on software metric threshold calculation techniques. In this study, electronic databases were systematically searched for relevant papers; 45 publications were selected based on inclusion/exclusion criteria, and research questions were answered. The results demonstrate the following important characteristics of studies: (a) both empirical and theoretical studies were conducted, a majority of which depends on empirical analysis; (b) the majority of papers apply statistical techniques to derive object-oriented metrics threshold values; (c) Chidamber and Kemerer (CK) metrics were studied in most of the papers, and are widely used to assess the quality of software systems; and (d) there is a considerable number of studies that have not validated metric threshold values in terms of quality attributes. From both the academic and practitioner points of view, the results of this review present a catalog and body of knowledge on metric threshold calculation techniques. The results set new research directions, such as conducting mixed studies on statistical and quality-related studies, studying an extensive number of metrics and studying interactions among metrics, studying more quality attributes, and considering multivariate threshold derivation.Article Citation Count: 117Test case prioritization: a systematic mapping study(Springer, 2013) Mıshra, Deepti; Mishra, Deepti; Computer EngineeringTest case prioritization techniques, which are used to improve the cost-effectiveness of regression testing, order test cases in such a way that those cases that are expected to outperform others in detecting software faults are run earlier in the testing phase. The objective of this study is to examine what kind of techniques have been widely used in papers on this subject, determine which aspects of test case prioritization have been studied, provide a basis for the improvement of test case prioritization research, and evaluate the current trends of this research area. We searched for papers in the following five electronic databases: IEEE Explorer, ACM Digital Library, Science Direct, Springer, and Wiley. Initially, the search string retrieved 202 studies, but upon further examination of titles and abstracts, 120 papers were identified as related to test case prioritization. There exists a large variety of prioritization techniques in the literature, with coverage-based prioritization techniques (i.e., prioritization in terms of the number of statements, basic blocks, or methods test cases cover) dominating the field. The proportion of papers on model-based techniques is on the rise, yet the growth rate is still slow. The proportion of papers that use datasets from industrial projects is found to be 64 %, while those that utilize public datasets for validation are only 38 %. On the basis of this study, the following recommendations are provided for researchers: (1) Give preference to public datasets rather than proprietary datasets; (2) develop more model-based prioritization methods; (3) conduct more studies on the comparison of prioritization methods; (4) always evaluate the effectiveness of the proposed technique with well-known evaluation metrics and compare the performance with the existing methods; (5) publish surveys and systematic review papers on test case prioritization; and (6) use datasets from industrial projects that represent real industrial problems.