Browsing by Author "Li, Yihao"
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Item A Bibliometric Assessment of Software Engineering Scholars and Institutions (2010-2017)(Elsevier Science Inc, 2018-10-22) Karanatsiou, Dimitra; Li, Yihao; Arvanitou, Elvira-Maria; Misirlis, Nikolaos; Wong, W. Eric; Li, Yihao; Wong, W. EricThis paper presents the findings of a bibliometric study, targeting an eight-year period (2010-2017), with the aim of identifying: (a) emerging research directions, (b) the top-20 institutions, and (c) top-20 early stage, consolidated, and experienced scholars in the field of software engineering. To perform this goal, we performed a bibliometric study, by applying the mapping study technique on top-quality software engineering venues, and developed a dataset of 14,456 primary studies. As the ranking metric for institutions, we used the count of papers in which authors affiliated with this institute have been identified in the obtained dataset, whereas regarding scholars we computed the corresponding rankings based on the number of published papers and the average number of citations. Finally, we identified the top-20 rising scholars in the SE research community, based on their recent publication record (between 2015 and 2017) and their research age.Item Applying Social Network Analysis to Software Fault-Proneness Prediction(2017-08) Li, Yihao; 65810187 (Wong, WE); Wong, W. EricDue to the rapid pace of software development, end-users now anticipate a seemingly limitless expansion of capabilities from their software. As a result, software systems are becoming increasingly complex and more susceptible to failures. Although software fault localization techniques are becoming more comprehensive, it is still expensive to precisely locate, let alone fix, bugs in a program. Hence, fault-proneness prediction can be applied beforehand to alleviate the cost of program debugging by identifying software modules which are likely to contain faults. Meanwhile, social network analysis (SNA) has been frequently applied in software engineering to depict relations between (1) modules, (2) developers, or (3) modules and developers. Previous studies have shown that these relations have been used to build social networks to predict fault-prone modules and the results are encouraging. Although these networks are useful for fault-proneness prediction, they are built either by a single relation or by a pair of relations aforementioned. In addition, these networks appear to neglect an essential factor: developer quality. After all, it is developers who make mistakes and introduce faults into software. We therefore, propose Tri-Relation Network (TRN), a weighted social network that integrates all three types of relations. Four network node centrality metrics are correspondingly derived from TRN. Moreover, a calibration mechanism for edge weights on TRN is explored as well. Case studies reveal that TRN holds great promise in the context of fault-proneness prediction and the effectiveness improves further after applying the calibration mechanism on current TRN.