Duplication Detection for Software Bug Reports Based on BM25 Term Weighting

Yang, Chengtao; Du, Hung-Hsueh; Wu, Sin-Sian; Chen, Ing-Xiang

doi:10.1109/taai.2012.20

Cited by 20 publications

(8 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In 2010, [21] used the BM25F to calculate the correlation between the bug reports. In year 2012 the same work of [21] was extended by [23] to calculate the effectiveness of BM25F for duplicate bug report detection. After these works, we observed that most of the works involve deep learning techniques for the same.…”

Section: Existing Approachesmentioning

confidence: 99%

See 1 more Smart Citation

A Systematic Study of Duplicate Bug Report Detection

Gupta¹,

Gupta²

2021

IJACSA

View full text Add to dashboard Cite

Defects are an integral part of any software project. They can arise at any time, at any phase of the software development or the maintenance phase. In open source projects, open bug repositories are used to maintain the bug reports. When a new bug report arrives, a person called "Triager" analyzes the bug report and assign it to some responsible developer. But before assigning, has to look if it is duplicate or not. Duplicate Bug Report is one of the big problems in the maintenance of bug repositories. Lack of knowledge and vocabulary skills of reporters sometimes increases the effort required for this purpose. Bug Tracking Systems are usually used to maintain the bug reports and are the most consulted resource during the maintenance process. Because of the Uncoordinated nature of the submission of bug reports to the tracking system, many times the same bug report is reported by many users. Duplicate Bug Reports lead to the waste of resources and the economy. It creates problems for triagers and requires a lot of analysis and validation. Lot of work has been done in the field of duplicate bug report detection. In this paper, we present the researches systematically done in this field by classifying the works into three categories and listing down the methods being used for the classified researches. The paper considers the papers till January 2020 for the analysis purpose. The paper mentions the strengths, limitations, data set, and the major approach used by the popular papers of the research in this field. The paper also lists the challenges and future directions in this field of research.

show abstract

Section: Existing Approachesmentioning

confidence: 99%

“…As the approach is based upon the word ordering, the presence of synonyms and alternate spellings create the problem [23] They did not considered the clustering information to their approach. [2] First paper where along with just the information from Bug Report, comments and user profile are also considered.…”

Section: σ(Wb)mentioning

confidence: 99%

A Systematic Study of Duplicate Bug Report Detection

Gupta¹,

Gupta²

2021

IJACSA

View full text Add to dashboard Cite

show abstract

“…2.4.3. BM25 approach for text similarity BM25 is a well-known ranking function that ranks matching relevant documents according to their relevance to a given search query ( ), regardless of the inter-relationship between the query terms within a document [56], [57]. It notices that 'query' in this study referred to meta-bug report.…”

Section: Matf Approach For Text Similaritymentioning

confidence: 99%

“…b is the free parameter of the normalization method for ( , ). It is only valid within [0, 1] but The standard setting for b should be 0.5 < b < 0.8[56],[58]. While 1 is also the free parameter used to control the value given by (1 − + × | | ).…”

mentioning

confidence: 99%

Automatic dependent bug reports assembly for bug tracking systems by threshold-based similarity

Luaphol

Polpinij²,

Kaneampornpan³

2021

IJEECS

View full text Add to dashboard Cite

<p>Bug reports contain essential information for fixing problems that occur in software. Many studies have proposed methods for automatic analysis of bug reports. One such task could affect the completion of software bug fixing, known as “bug dependency”. Although this problem was mentioned by many researches, most of them discussed about the related bugs but not really dealt with dependency issue in bug reports. One possible solution used for addressing this issue is to assemble all relevant/dependent bug reports together before analysis of the next processing stages. This study presents a method of assembling dependent bug reports. The main mechanism is called “threshold-based similarity analysis”, and the three similarity techniques of cosine similarity (CS) multi aspect TF (MATF), and BM25 are compared with feedback, precision and likelihood value. As the BM25 with the threshold as 0.5 gives the best results, it was used to compare with the state of the art method. The results show that our method increases precision and likelihood values by 12% and 12.4% respectively. Therefore, our results can be used to encourage developers to recognize all dependent bugs in the same problem domain.</p>

show abstract

“…In addition to textual content in summary and description of bug reports, other non-textual aspects including product, component, and version were also utilized for detection. They also extended BM25F [15], which is a document similarity formula built upon Tf-Idf.…”

Section: Related Workmentioning

confidence: 99%

Automated Duplicate Bug Report Detection Using Multi-Factor Analysis

Zou

Yang

et al. 2016

IEICE Trans. Inf. & Syst.

View full text Add to dashboard Cite

SUMMARYThe bug reports expressed in natural language text usually suffer from vast, ambiguous and poorly written, which causes the challenge to the duplicate bug reports detection. Current automatic duplicate bug reports detection techniques have mainly focused on textual information and ignored some useful factors. To improve the detection accuracy, in this paper, we propose a new approach calls LNG (LDA and N-gram) model which takes advantages of the topic model LDA and word-based model Ngram. The LNG considers multiple factors, including textual information, semantic correlation, word order, contextual connections, and categorial information, that potentially affect the detection accuracy. Besides, the Ngram adopted in our LNG model is improved by modifying the similarity algorithm. The experiment is conducted under more than 230,000 real bug reports of the Eclipse project. In the evaluation, we propose a new evaluation metric, namely exact-accuracy (EA) rate, which can be used to enhance the understanding of the performance of duplicates detection. The evaluation results show that all the recall rate, precision rate, and EA rate of the proposed method are higher than treating them separately. Also, the recall rate is improved by 2.96%-10.53% compared to the state-of-art approach DBTM.

show abstract

Duplication Detection for Software Bug Reports Based on BM25 Term Weighting

Cited by 20 publications

References 11 publications

A Systematic Study of Duplicate Bug Report Detection

A Systematic Study of Duplicate Bug Report Detection

Automatic dependent bug reports assembly for bug tracking systems by threshold-based similarity

Automated Duplicate Bug Report Detection Using Multi-Factor Analysis

Contact Info

Product

Resources

About