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2017 IEEE Workshop on Machine Learning Techniques for Software Quality Evaluation (MaLTeSQuE)

21-21 Feb. 2017

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Displaying Results 1 - 10 of 10
  • [Front cover]

    Publication Year: 2017, Page(s): c1
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  • Author index

    Publication Year: 2017, Page(s): 39
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  • Table of contents

    Publication Year: 2017, Page(s): v
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  • Message from the Chairs

    Publication Year: 2017, Page(s):iii - iv
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  • Using source code metrics to predict change-prone web services: A case-study on ebay services

    Publication Year: 2017, Page(s):1 - 7
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (242 KB) | HTML iconHTML

    Predicting change-prone object-oriented software using source code metrics is an area that has attracted several researchers attention. However, predicting change-prone web services in terms of changes in the WSDL (Web Service Description Language) Interface using source code metrics implementing the services is a relatively unexplored area. We conduct a case-study on change proneness prediction o... View full abstract»

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  • Investigating code smell co-occurrences using association rule learning: A replicated study

    Publication Year: 2017, Page(s):8 - 13
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (213 KB) | HTML iconHTML

    Previous research demonstrated how code smells (i.e., symptoms of the presence of poor design or implementation choices) threat software maintainability. Moreover, some studies showed that their interaction has a stronger negative impact on the ability of developers to comprehend and enhance the source code when compared to cases when a single code smell instance affects a code element (i.e., a cl... View full abstract»

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  • Using machine learning to design a flexible LOC counter

    Publication Year: 2017, Page(s):14 - 20
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (291 KB) | HTML iconHTML

    The results of counting the size of programs in terms of Lines-of-Code (LOC) depends on the rules used for counting (i.e. definition of which lines should be counted). In the majority of the measurement tools, the rules are statically coded in the tool and the users of the measurement tools do not know which lines were counted and which were not. The goal of our research is to investigate how to u... View full abstract»

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  • Machine learning for finding bugs: An initial report

    Publication Year: 2017, Page(s):21 - 26
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (148 KB) | HTML iconHTML

    Static program analysis is a technique to analyse code without executing it, and can be used to find bugs in source code. Many open source and commercial tools have been developed in this space over the past 20 years. Scalability and precision are of importance for the deployment of static code analysis tools - numerous false positives and slow runtime both make the tool hard to be used by develop... View full abstract»

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  • Automatic feature selection by regularization to improve bug prediction accuracy

    Publication Year: 2017, Page(s):27 - 32
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (228 KB) | HTML iconHTML

    Bug prediction has been a hot research topic for the past two decades, during which different machine learning models based on a variety of software metrics have been proposed. Feature selection is a technique that removes noisy and redundant features to improve the accuracy and generalizability of a prediction model. Although feature selection is important, it adds yet another step to the process... View full abstract»

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  • Hyperparameter optimization to improve bug prediction accuracy

    Publication Year: 2017, Page(s):33 - 38
    Request permission for commercial reuse | Click to expandAbstract | PDF file iconPDF (204 KB) | HTML iconHTML

    Bug prediction is a technique that strives to identify where defects will appear in a software system. Bug prediction employs machine learning to predict defects in software entities based on software metrics. These machine learning models usually have adjustable parameters, called hyperparameters, that need to be tuned for the prediction problem at hand. However, most studies in the literature ke... View full abstract»

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