Towards Accurate Duplicate Bug Retrieval Using Deep Learning Techniques

Deshmukh, Jayati; M, Annervaz K.; Podder, Sanjay; Sengupta, Shamik; Dubash, Neville

doi:10.1109/icsme.2017.69

Cited by 77 publications

(53 citation statements)

References 19 publications

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“…Note that, for the abbreviation of these approaches, we prefix them with the abbreviation of category name for better understanding. Another note is that, for the approaches which do not have a name in their original paper, e.g., [12,36,38,40,55], we name them based on their main idea to facilitate reading.…”

Section: Study Quality Assessmentmentioning

confidence: 99%

“…The third category contains five approaches, i.e., DL-DCNN [38], DL-MALSTM [29], DL-CWEIR [55], DL-BiMPM [53], and DL-CRNN [12]. The main idea of them is to model the semantic similarity of reports with deep learning techniques for duplicate detection.…”

Section: Deep Learning (Dl) Based Approachesmentioning

confidence: 99%

“…Following existing studies [12,30,36,39,40,55,57], we use @ and as the metrics for measuring the performance of duplicate detection approaches.…”

Section: Evaluation Metricsmentioning

confidence: 99%

“…[39,40,57] designed a set of features for measuring the reports' similarity in terms of textual descriptions and attributes, and employed machine learning techniques for duplicate detection. [12,29,38,53,55] modeled the semantic similarity of reports using deep learning techniques for duplicate detection. Despite of that, different studies employed different mechanisms or features for duplicate detection, and evaluated on different datasets.…”

Section: Introductionmentioning

confidence: 99%

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Quest for the Golden Approach

Huang

Wang

et al. 2020

Proceedings of the 14th ACM / IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM)

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Background: Given the invisibility and unpredictability of distributed crowdtesting processes, there is a large number of duplicate reports, and detecting these duplicate reports is an important task to help save testing effort. Although, many approaches have been proposed to automatically detect the duplicates, the comparison among them and the practical guidelines to adopt these approaches in crowdtesting remain vague. Aims: We aim at conducting the first experimental evaluation of the commonly-used and state-of-the-art approaches for duplicate detection in crowdtesting reports, and exploring which is the golden approach. Method: We begin with a systematic review of approaches for duplicate detection, and select ten state-of-the-art approaches for our experimental evaluation. We conduct duplicate detection with each approach on 414 crowdtesting projects with 59,289 reports collected from one of the largest crowdtesting platforms. Results: Machine learning based approach, i.e., ML-REP, and deep learning based approach, i.e., DL-BiMPM, are the best two approaches for duplicate reports detection in crowdtesting, while the later one is more sensitive to the size of training data and more time-consuming for model training and prediction. Conclusions: This paper provides new insights and guidelines to select appropriate duplicate detection techniques for duplicate crowdtesting reports detection. CCS CONCEPTS • Software and its engineering → Software testing and debugging.

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Section: Study Quality Assessmentmentioning

confidence: 99%

Section: Deep Learning (Dl) Based Approachesmentioning

confidence: 99%

“…Following existing studies [12,30,36,39,40,55,57], we use @ and as the metrics for measuring the performance of duplicate detection approaches.…”

Section: Evaluation Metricsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Quest for the Golden Approach

Huang

Wang

et al. 2020

Proceedings of the 14th ACM / IEEE International Symposium on Empirical Software Engineering and Measurement (ESEM)

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show abstract

“…Unlike previous literature, we also consider Deep Learning techniques [15,25], which have gained importance in recent years. In the past, their employment have mainly been on Computer Vision, Natural Language Processing and Speech Recognition [26].…”

Section: Study Setupmentioning

confidence: 99%

Data Mining Techniques for Software Quality Prediction in Open Source Software

Canaparo

Ronchieri

2019

EPJ Web Conf.

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Software quality monitoring and analysis are among the most productive topics in software engineering research. Their results may be effectively employed by engineers during software development life cycle. Open source software constitutes a valid test case for the assessment of software characteristics. The data mining approach has been proposed in literature to extract software characteristics from software engineering data. This paper aims at comparing diverse data mining techniques (e.g., derived from machine learning) for developing effective software quality prediction models. To achieve this goal, we tackled various issues, such as the collection of software metrics from open source repositories, the assessment of prediction models to detect software issues and the adoption of statistical methods to evaluate data mining techniques. The results of this study aspire to identify the data mining techniques that perform better amongst all the ones used in this paper for software quality prediction models. *

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TagDeepRec: Tag Recommendation for Software Information Sites Using Attention-Based Bi-LSTM

Yan

et al. 2019

Knowledge Science, Engineering and Management

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Towards Accurate Duplicate Bug Retrieval Using Deep Learning Techniques

Cited by 77 publications

References 19 publications

Quest for the Golden Approach

Quest for the Golden Approach

Data Mining Techniques for Software Quality Prediction in Open Source Software

TagDeepRec: Tag Recommendation for Software Information Sites Using Attention-Based Bi-LSTM

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