At present, under the background of the vigorous development of Internet technology and the era of the Internet of Everything, the diversification and complexity of software application scenarios are growing at an unp...
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This paper proposes an innovative approach for enhancing the efficiency and security of manufacturing supply chains by integrating the Internet of Things, blockchain technology, and genetic algorithm-based consensus m...
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Many studies have explored the methods of deriving thresholds of object-oriented (i.e. OO) metrics. Unsupervised methods are mainly based on the distributions of metric values, while supervised methods principally res...
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Many studies have explored the methods of deriving thresholds of object-oriented (i.e. OO) metrics. Unsupervised methods are mainly based on the distributions of metric values, while supervised methods principally rest on the relationships between metric values and defect-proneness of classes. The objective of this study is to empirically examine whether there are effective threshold values of OO metrics by analyzing existing threshold derivation methods with a large-scale meta-analysis. Based on five representative threshold derivation methods (i.e. VARL, ROC, BPP, MFM, and MGM) and 3268 releases from 65 Java projects, we first employ statistical meta-analysis and sensitivity analysis techniques to derive thresholds for 62 OO metrics on the training data. Then, we investigate the predictive performance of five candidate thresholds for each metric on the validation data to explore which of these candidate thresholds can be served as the threshold. Finally, we evaluate their predictive performance on the test data. The experimental results show that 26 of 62 metrics have the threshold effect and the derived thresholds by meta-analysis achieve promising results of GM values and significantly outperform almost all five representative (baseline) thresholds.
Effective sharing and reuse practices have long been hallmarks of proficient softwareengineering. Yet the exploratory nature of data science presents new challenges and opportunities to support sharing and reuse of a...
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ISBN:
(纸本)9781665495905
Effective sharing and reuse practices have long been hallmarks of proficient softwareengineering. Yet the exploratory nature of data science presents new challenges and opportunities to support sharing and reuse of analysis code. To better understand current practices, we conducted interviews (N=17) and a survey (N=132) with data scientists at Microsoft, and extract five commonly used strategies for sharing and reuse of past work: personal analysis reuse, personal utility libraries, team shared analysis code, team shared template notebooks, and team shared libraries. We also identify factors that encourage or discourage data scientists from sharing and reusing. Our participants described obstacles to reuse and sharing including a lack of incentives to create shared code, difficulties in making data science code modular, and a lack of tool interoperability. We discuss how future tools might help meet these needs.
Kindness can boost happiness and wellbeing. It can benefit individuals (e.g., increasing resilience) as well as society (e.g., increasing trust). With digital technology permeating our daily lives, there are increasin...
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ISBN:
(纸本)9781665495967
Kindness can boost happiness and wellbeing. It can benefit individuals (e.g., increasing resilience) as well as society (e.g., increasing trust). With digital technology permeating our daily lives, there are increasing opportunities for such technology to enable, mediate, and amplify kindness in society. In this paper, we propose kind computing, a new computing paradigm that explicitly incorporates kindness into the development and use of digital technology. We envisage softwareengineering as a discipline that can deliver such technology. However, softwareengineering techniques do not provide explicit abstractions, formalisms, and tools to consider, analyse, and implement software that delivers such technology. With reference to related work, we elaborate on kind computing and the role of softwareengineering in enabling it, identify open research challenges, elicit three categories of kind computing requirements, and sketch a research agenda for future work.
Failures that are not related to a specific fault can reduce the effectiveness of fault localization in multi-fault scenarios. To tackle this challenge, researchers and practitioners typically cluster failures (e.g., ...
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ISBN:
(纸本)9781450394758
Failures that are not related to a specific fault can reduce the effectiveness of fault localization in multi-fault scenarios. To tackle this challenge, researchers and practitioners typically cluster failures (e.g., failed test cases) into several disjoint groups, with those caused by the same fault grouped together. In such a fault isolation process that requires input in a mathematical form, ranking-based failure proximity (R-proximity) is widely used to model failed test cases. In R-proximity, each failed test case is represented as a suspiciousness ranking list of program statements through a fingerprinting function (i.e., a risk evaluation formula, REF). Although many offthe-shelf REFs have been integrated into R-proximity, they were designed for single-fault localization originally. To the best of our knowledge, no REF has been developed to serve as a fingerprinting function of R-proximity in multi-fault scenarios. For better clustering failures in fault isolation, in this paper, we present a genetic programming-based framework along with a sophisticated fitness function, for evolving REFs with the goal of more properly representing failures in multi-fault scenarios. By using a small set of programs for training, we get a collection of REFs that can obtain good results applicable in a larger and more general scale of scenarios. The best one of them outperforms the state-of-the-art by 50.72% and 47.41% in faults number estimation and clustering effectiveness, respectively. Our framework is highly configurable for further use, and the evolved formulas can be directly applied in future failure representation tasks without any retraining.
作者：
Apon, Imtiaz AhamedHasan, Md RatulHaque, Md. Salman
Department of Electrical and Electronic Engineering Saidpur5311 Bangladesh
Department of Materials Science and Engineering Khulna9203 Bangladesh
Department of Materials and Metallurgical Engineering Dhaka1000 Bangladesh
Very-large-scale integration (VLSI) of elliptic curve cryptography (ECC) is vital for efficiently securing the digital world. This research demonstrates a description of elliptic curve cryptography (ECC), with a focus...
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Blockchain technology and smart contracts are pivotal innovations in the digital era, offering a paradigm shift in how data is stored, shared, and verified across decentralized networks. Initially conceived to underpi...
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Systematic literature reviews (SLRs) and systematic mapping studies (SMSs) are common studies in any discipline to describe and classify past works, and to inform a research field of potential new areas of investigati...
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ISBN:
(纸本)9789897586477
Systematic literature reviews (SLRs) and systematic mapping studies (SMSs) are common studies in any discipline to describe and classify past works, and to inform a research field of potential new areas of investigation. This last task is typically achieved by observing gaps in past works, and hinting at the possibility of future research in those gaps. Using an NLP-driven methodology, this paper proposes a meta-analysis to extend current systematic methodologies of literature reviews and mapping studies. Our work leverages a Word2Vec model, pre-trained in the softwareengineering domain, and is combined with a time series analysis. Our aim is to forecast future trajectories of research outlined in systematic studies, rather than just describing them. Using the same dataset from our own previous mapping study, we were able to go beyond descriptively analysing the data that we gathered, or to barely 'guess' future directions. In this paper, we show how recent advancements in the field of our SMS, and the use of time series, enabled us to forecast future trends in the same field. Our proposed methodology sets a precedent for exploring the potential of language models coupled with time series in the context of systematically reviewing the literature.
To meet the time requested for software industry needs, small and medium enterprises use agile methods, such as Serum, to produce software in a shorter development cycle. However, the lack of knowledge on how to imple...
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ISBN:
(纸本)9781665461269
To meet the time requested for software industry needs, small and medium enterprises use agile methods, such as Serum, to produce software in a shorter development cycle. However, the lack of knowledge on how to implement and use agile methods correctly results in an empirical adoption of them, most of the time without getting the expected results like inefficient software development. This paper presents a proposal to evaluate agile methods, specifically Serum. The method is addressed to very small organizations (VSEs) and small and medium-sized enterprises (SMEs). In addition, the method includes a tool for its implementation that allows evaluating organizations and offering the required information to implement Serum and increase the organization's maturity using Serum.
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