225 research outputs found

    Easy over Hard: A Case Study on Deep Learning

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    While deep learning is an exciting new technique, the benefits of this method need to be assessed with respect to its computational cost. This is particularly important for deep learning since these learners need hours (to weeks) to train the model. Such long training time limits the ability of (a)~a researcher to test the stability of their conclusion via repeated runs with different random seeds; and (b)~other researchers to repeat, improve, or even refute that original work. For example, recently, deep learning was used to find which questions in the Stack Overflow programmer discussion forum can be linked together. That deep learning system took 14 hours to execute. We show here that applying a very simple optimizer called DE to fine tune SVM, it can achieve similar (and sometimes better) results. The DE approach terminated in 10 minutes; i.e. 84 times faster hours than deep learning method. We offer these results as a cautionary tale to the software analytics community and suggest that not every new innovation should be applied without critical analysis. If researchers deploy some new and expensive process, that work should be baselined against some simpler and faster alternatives.Comment: 12 pages, 6 figures, accepted at FSE201

    Learning representations for effective and explainable software bug detection and fixing

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    Software has an integral role in modern life; hence software bugs, which undermine software quality and reliability, have substantial societal and economic implications. The advent of machine learning and deep learning in software engineering has led to major advances in bug detection and fixing approaches, yet they fall short of desired precision and recall. This shortfall arises from the absence of a \u27bridge,\u27 known as learning code representations, that can transform information from source code into a suitable representation for effective processing via machine and deep learning. This dissertation builds such a bridge. Specifically, it presents solutions for effectively learning code representations using four distinct methods?context-based, testing results-based, tree-based, and graph-based?thus improving bug detection and fixing approaches, as well as providing developers insight into the foundational reasoning. The experimental results demonstrate that using learning code representations can significantly enhance explainable bug detection and fixing, showcasing the practicability and meaningfulness of the approaches formulated in this dissertation toward improving software quality and reliability

    Integration of an Automatic Fault Localization Tool in an IDE and its Evaluation

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    Debugging is one of the most demanding and error-prone tasks in software development. Trying to address bugs has become overall more expensive as the software complexity and size have increased. As a result, several researchers attempted to improve the developers’ debugging experience and efficiency by automating as much of the process as possible. Existing auto-finding tools will assist developers in automatically detecting bugs, however, they are not yet widely available to software engineers. Making such tools available to developers can save debugging time and increase the productivity. Subsequently, the main goal of this dissertation is to incorporate an automatic fault localization tool into an Integrated Development Environment (IDE). The selected IDE was Visual Studio Code, a source-code editor developed by Microsoft for Windows, Linux, and macOS. Visual Studio Code is one of the most used IDEs and is known for its flexible API, which allows nearly every aspect of it to be customized. Furthermore, the chosen automatic fault localization tool was FLACOCO, a recent fault localization tool for Java that supports up to the most recent versions. Nonetheless, this document contains a full overview of several fault localization methodologies and tools, as well as an explanation of the complete planning and development process of the produced Visual Studio Code extension. After the development and deployment were completed, an evaluation was carried out. The extension was evaluated through a user study in which thirty Java professionals took part. The test had two parts: the first involved users using the extension to complete two debugging tasks in previously unknown projects, and the second had them filling out a satisfaction questionnaire for further analysis. Finally, the results show that the extension was a success, with the system being rated positively in all areas. However, it may be revised in light of the questionnaire responses, with the suggestions received being considered for future work.A depuração é uma das tarefas mais exigentes e propensas a erros no desenvolvimento de software. Tentar resolver esses erros tornou-se mais dispendioso com os incrementos de complexidade e tamanho do software. Deste modo, ao longo dos últimos anos, vários investigadores tentaram melhorar a experiência de depuração e a eficiência dos desenvolvedores automatizando o máximo possível do processo. Existem ferramentas de localização de defeitos que assistem os desenvolvedores na detecção automática de bugs, no entanto estas ainda não se encontram amplamente disponíveis para os programadores. Tornar essas ferramentas disponíveis para todos certamente iria resultar na redução do tempo de depuração e no aumento da produtividade. Assim sendo, o principal objetivo desta dissertação é incorporar uma ferramenta de localização automática de defeitos num IDE. Em termos de IDE, o Visual Studio Code, um editor de código-fonte desenvolvido pela Microsoft para Windows, Linux e macOS, foi selecionado. Este IDE tem ganho bastante popularidade, sendo um dos IDEs mais utilizados mundialmente. Além disso, o Visual Studio Code é reconhecido pela sua API flexível, que permite que quase todos os seus aspectos sejam personalizados. Adicionalmente, o FLACOCO, uma ferramenta de localização de defeitos baseada em SFL que suporta até as versões mais recentes do Java, foi escolhida como ferramenta de localização automática de defeitos. Além do mais, esta dissertação contém um estudo sobre as técnicas de localização automática de defeitos e as suas ferramentas, bem como uma explicação do planeamento e implementação da extensão criada para o Visual Studio Code. Após o término da implementação e a posterior implantação, foi efetuada a sua avaliação. Procedeu-se a um teste de utilização com a participação de treze utilizadores proficientes na linguagem Java. O teste foi composto por duas componentes: na primeira os utilizadores utilizaram a extensão para completar duas tarefas de depuração em projetos por eles desconhecidos e na segunda foi-lhes fornecido um questionário de satisfação para posterior análise. Os resultados obtidos sugerem que a extensão foi um sucesso, sendo que o sistema foi positivamente avaliado em todos os aspetos. No entanto a mesma poderá ser aprimorada tendo em consideração o feedback obtido na secção de resposta livre do questionário, sendo que o mesmo foi bastante valioso e as sugestões apuradas vieram a ser consideradas para trabalho futuro

    Using Machine Learning to Generate Test Oracles: A Systematic Literature Review

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    Machine learning may enable the automated generation of test oracles. We have characterized emerging research in this area through a systematic literature review examining oracle types, researcher goals, the ML techniques applied, how the generation process was assessed, and the open research challenges in this emerging field.Based on a sample of 22 relevant studies, we observed that ML algorithms generated test verdict, metamorphic relation, and - most commonly - expected output oracles. Almost all studies employ a supervised or semi-supervised approach, trained on labeled system executions or code metadata - including neural networks, support vector machines, adaptive boosting, and decision trees. Oracles are evaluated using the mutation score, correct classifications, accuracy, and ROC. Work-to-date show great promise, but there are significant open challenges regarding the requirements imposed on training data, the complexity of modeled functions, the ML algorithms employed - and how they are applied - the benchmarks used by researchers, and replicability of the studies. We hope that our findings will serve as a roadmap and inspiration for researchers in this field

    Using Machine Learning to Generate Test Oracles: A Systematic Literature Review

    Get PDF
    Machine learning may enable the automated generation of test oracles. We have characterized emerging research in this area through a systematic literature review examining oracle types, researcher goals, the ML techniques applied, how the generation process was assessed, and the open research challenges in this emerging field. Based on a sample of 22 relevant studies, we observed that ML algorithms generated test verdict, metamorphic relation, and - most commonly - expected output oracles. Almost all studies employ a supervised or semi-supervised approach, trained on labeled system executions or code metadata - including neural networks, support vector machines, adaptive boosting, and decision trees. Oracles are evaluated using the mutation score, correct classifications, accuracy, and ROC. Work-to-date show great promise, but there are significant open challenges regarding the requirements imposed on training data, the complexity of modeled functions, the ML algorithms employed - and how they are applied - the benchmarks used by researchers, and replicability of the studies. We hope that our findings will serve as a roadmap and inspiration for researchers in this field.Comment: Pre-print. Article accepted to 1st International Workshop on Test Oracles at ESEC/FSE 202

    Using multiclass classification algorithms to improve text categorization tool:NLoN

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    Abstract. Natural language processing (NLP) and machine learning techniques have been widely utilized in the mining software repositories (MSR) field in recent years. Separating natural language from source code is a pre-processing step that is needed in both NLP and the MSR domain for better data quality. This paper presents the design and implementation of a multi-class classification approach that is based on the existing open-source R package Natural Language or Not (NLoN). This article also reviews the existing literature on MSR and NLP. The review classified the information sources and approaches of MSR in detail, and also focused on the text representation and classification tasks of NLP. In addition, the design and implementation methods of the original paper are briefly introduced. Regarding the research methodology, since the research goal is technology-oriented, i.e., to improve the design and implementation of existing technologies, this article adopts the design science research methodology and also describes how the methodology was adopted. This research implements an open-source Python library, namely NLoN-PY. This is an open-source library hosted on GitHub, and users can also directly use the tools published to the PyPI library. Since NLoN has achieved comparable performance on two-class classification tasks with the Lasso regression model, this study evaluated other multi-class classification algorithms, i.e., Naive Bayes, k-Nearest Neighbours, and Support Vector Machine. Using 10-fold cross-validation, the expanded classifier achieved AUC performance of 0.901 for the 5-class classification task and the AUC performance of 0.92 for the 2-class task. Although the design of this study did not show a significant performance improvement compared to the original design, the impact of unbalanced data distribution on performance was detected and the category of the classification problem was also refined in the process. These findings on the multi-class classification design can provide a research foundation or direction for future research
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