1,720,997 research outputs found
Adaptive selection of classifiers for bug prediction: A large-scale empirical analysis of its performances and a benchmark study
No full textBug prediction aims at locating defective source code components relying on machine learning models. Although some previous work showed that selecting the machine-learning classifier is crucial, the results are contrasting. Therefore, several ensemble techniques, i.e., approaches able to mix the output of different classifiers, have been proposed. In this paper, we present a benchmark study in which we compare the performance of seven ensemble techniques on 21 open-source software projects. Our aim is twofold. On the one hand, we aim at bridging the limitations of previous empirical studies that compared the accuracy of ensemble approaches in bug prediction. On the other hand, our goal is to verify how ensemble techniques perform in different settings such as cross- and local-project defect prediction. Our empirical experimentation results show that ensemble techniques are not a silver bullet for bug prediction. In within-project bug prediction, using ensemble techniques improves the prediction performance with respect to the best stand-alone classifier. We confirm that the models based on VALIDATION AND VOTING achieve slightly better results. However, they are similar to those obtained by other ensemble techniques. Identifying buggy classes using external sources of information is still an open problem. In this setting, the use of ensemble techniques does not provide evident benefits with respect to stand-alone classifiers. The statistical analysis highlights that local and global models are mostly equivalent in terms of performance. Only one ensemble technique (i.e., ASCI) slightly exploits local learning to improve performance
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Preface for “Quantum Programming for Software Engineering (QP4SE)”
No full textThe advent of quantum computing is poised to revolutionize computer science as we know it. The “quantum era” is just around the corner, promising to transform computation to the extent that solving NP-complete problems may become feasible. Major software companies, including IBM, Google, and others, are investing hundreds of billions of dollars to develop novel hardware and software tools to achieve quantum supremacy and embrace this paradigm shift. However, large-scale pure quantum software systems remain a distant goal, creating growing interest in the development of hybrid Quantum-Based Software Systems (QBSs), where quantum components are integrated into traditional software applications. This emerging landscape necessitates the establishment of design principles, quality assurance frameworks, and verification and validation practices for Quantum Programming, forming the Software Engineering for Quantum Programming (SE4QP) research domain. At the same time, it is equally crucial to explore the complementary perspective: Quantum Programming for Software Engineering (QP4SE).
Many Software Engineering challenges demand significant computational resources. Tasks such as the automatic generation and optimization of test suites or the application of artificial intelligence techniques to evaluate and improve software quality often require substantial effort to complete within a reasonable timeframe. Recently, quantum technologies have demonstrated their potential to address problems that are computationally infeasible with traditional approaches. In the past few years, numerous quantum based solutions have emerged. Quantum optimization algorithms have been proposed as more efficient alternatives to conventional optimization techniques. Similarly, the rise of quantum machine learning has garnered attention from researchers and practitioners, offering quantum-based counterparts to traditional ML algorithms, such as Quantum Support Vector Machines (QSVM).
In this context, researchers have begun applying quantum-based approaches to Software Engineering problems to enhance the performance of existing solutions. Quantum programming, therefore, holds great promise for solving domain-specific challenges in Software Engineering or improving current methods’ efficiency. This highlights the need for researchers and practitioners to devise and evaluate new methodologies for applying Quantum Programming to Software Engineering. To address these needs, Quantum Programming for Software Engineering (QP4SE) has served as a forum for researchers and practitioners to present and discuss the latest challenges in this area while sharing best practices for developing innovative solutions
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
QP4SE Welcome from the chairs
No full textWe propose the 1st edition of the workshop on Quantum Programming for Software Engineering (QP4SE) to be co-located with the next edition of ESEC/FSE 2022. Quantum Programming is gaining more and more importance over the last years, since both researchers and practitioners believe that this new technology could substantially change the world of computation, leading to solving problems for which there is no solution yet. The aim of this workshop is to create a community for both researchers and practitioners to discuss new ideas and present new results on the application of Quantum Programming to provide new solutions, or improve the existing ones, for specific Software Engineering problems
VITRuM: A Plug-In for the Visualization of Test-Related Metrics
No full textSoftware testing is the first weapon against software faults, used by developers to preventively locate implementation errors in the exercised production code that may cause critical failures to the inner-working of software systems. According to recent findings, the effectiveness of testing might be not only due to its ability to cover the production code but also to some other properties, like code quality. Among other aspects, the literature reported that an advanced visualization of test-related metrics, e.g., test code coverage on production code, result to be a key strength for developers when dealing with software faults. In this paper, we propose VITRuM (VIsualization of Test-Related Metrics), an IntelliJ plug-in able to provide developers with an advanced visual interface of both static and dynamic test-related metrics that has the potential of making them more able to diagnose production code faults. The plug-in is available in the official JetBrains Plugins Repository. A video showing the tool in action is available at https://youtu.be/kFE81eYPgUg
Dispelling the Myths Behind First-author Citation Counts
Full text linkWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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