Network-Clustered Multi-Modal Bug Localization

Hoang, Thong; Oentaryo, Richard J.; Le, Tien-Duy B.; Lo, David

doi:10.1109/tse.2018.2810892

Cited by 31 publications

(16 citation statements)

References 72 publications

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“…Several IR-based bug localizing methods have been suggested using information retrieval techniques to reformulate queries (Chaparro et al 2019;Rahman and Roy 2018;Koyuncu et al 2019) and improve the performance of bug localization (Lee et al 2018;Tantithamthavorn et al 2018), the relation between bug report and source code (Khatiwada et al 2018;Le et al 2017), and clustering bug report and program elements (Hoang et al 2018). Lam et al (2015) performed an empirical study to improve bug report handling by automating the task of associating buggy files with a bug report.…”

Section: Ir-based Bug Localizationmentioning

confidence: 99%

On the classification of bug reports to improve bug localization

Fang

et al. 2021

Soft Comput

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Bug localization is the automated process of finding the possible faulty files in a software project. Bug localization allows developers to concentrate on vital files. Information retrieval (IR)-based approaches have been proposed to assist automatically identify software defects by using bug report information. However, some bug reports that are not semantically related to the relevant code are not helpful to IR-based systems. Running an IR-based reporting system can lead to false-positive results. In this paper, we propose a classification model for classifying a bug report as either uninformative or informative. Our approach helps to lower false positives and increase ranking performances by filtering uninformative information before running an IR-based bug location system. The model is based on implicit features learned from bug reports that use neural networks and explicit features defined manually. We test our proposed model on three open-source software projects that contain over 9000 bug reports. The results of the evaluation show that our model enhances the efficiency of a developed IR-based system in the trade-off between precision and recall. For implicit features, our tests with comparisons show that the LSTM network performs better than the CNN and multilayer perceptron with respect to the F-measurements. Combining both implicit and explicit features outperforms using only implicit features. Our classification model helps improve precision in bug localization tasks when precision is considered more important than recall.

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Section: Ir-based Bug Localizationmentioning

confidence: 99%

On the classification of bug reports to improve bug localization

Fang

et al. 2021

Soft Comput

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“…(Huo, Li, and Zhou 2016) encodes bug reports and source code through different structures and only fuse their features right before classification. (Hoang et al 2018;Tantithamthavorn et al 2018) directly calculate similarity scores between bug reports and code snippets after separately encoding and include them as a component of the data pair's combined features. However, treating text sentence and code snippet differently can neither well resolve the semantic gap between two modalities nor make use of the complementary knowledge in between.…”

Section: Related Workmentioning

confidence: 99%

“…Among those tasks, natural language and programming language are two common data types, which require feature extraction as a preliminary step so that their representation can be processed by specific models. The extracted features have been used to address tasks such as bug localization (Huo, Li, and Zhou 2016;Huo and Li 2017;Hoang et al 2018), code clone detection (White et al 2016;Wei and Li 2017;, code summarization (Allamanis, Peng, and Sutton 2016), etc. However, one main factor that determines the capacity of representation lies in the information exploited from data, since partial semantic information can result in features of poor quality, and thus prevent models from elevating performance.…”

Section: Introductionmentioning

confidence: 99%

Learning Uniform Semantic Features for Natural Language and Programming Language Globally, Locally and Sequentially

Zhang¹,

Zheng²,

Li³

2019

AAAI

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Semantic feature learning for natural language and programming language is a preliminary step in addressing many software mining tasks. Many existing methods leverage information in lexicon and syntax to learn features for textual data. However, such information is inadequate to represent the entire semantics in either text sentence or code snippet. This motivates us to propose a new approach to learn semantic features for both languages, through extracting three levels of information, namely global, local and sequential information, from textual data. For tasks involving both modalities, we project the data of both types into a uniform feature space so that the complementary knowledge in between can be utilized in their representation. In this paper, we build a novel and general-purpose feature learning framework called UniEmbed, to uniformly learn comprehensive semantic representation for both natural language and programming language. Experimental results on three real-world software mining tasks show that UniEmbed outperforms state-of-the-art models in feature learning and prove the capacity and effectiveness of our model.

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“…In software development and maintenance, developers often spend much effort and resources for debugging [8]. Defect identification and localization aim to help developers save time in finding and locating suspicious defective program elements, such as defective lines of code.…”

Section: Introductionmentioning

confidence: 99%

JITO: a tool for just-in-time defect identification and localization

Qiu

Yan

Xia

et al. 2020

Proceedings of the 28th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Softw

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In software development and maintenance, defect localization is necessary for software quality assurance. Current defect localization techniques mainly rely on defect symptoms (e.g., bug reports or program spectrum) when the defect has been exposed. One challenge task is: can we locate buggy program prior to the appearance of the defect symptom. Such kind of localization is conducted at an early stage (e.g., when buggy program elements are being checkedin) which can be an early step of continuous quality control. In this paper, we propose a Just-In-Time defect identification and lOcalization tool, named JITO, which can help developers to locate defective lines at check-in time. In summary, JITO contains two phases: (i) identify if a new change is buggy and (ii) locate suspicious buggy code lines in the identified buggy changes. We implement JITO as a plugin in an integrated development environment (i.e., Intellij IDEA). When developers using our plugin, JITO loads the local Git repository to build the JIT defect identification model and localization model based on historical changes. After submitting a new change to the local repository, developers apply JITO to identify whether it is a buggy change. If a buggy change is identified, JITO leverages JIT defect localization model to locate its suspicious buggy lines and highlight them in Intellij IDEA. Experimental results show that JITO outperforms two baselines (i.e., random guess and a static bug finder (i.e., PMD)) by a substantial margin in terms of four ranking measures. Demo URL: https://youtu.be/tvnYs62FkEQ Plugin download: https://git.io/Jf5r1 CCS CONCEPTS • Software and its engineering → Software development techniques.

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Network-Clustered Multi-Modal Bug Localization

Cited by 31 publications

References 72 publications

On the classification of bug reports to improve bug localization

On the classification of bug reports to improve bug localization

Learning Uniform Semantic Features for Natural Language and Programming Language Globally, Locally and Sequentially

JITO: a tool for just-in-time defect identification and localization

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