Detection of Duplicate Defect Reports Using Natural Language Processing

By Per Runeson, Magnus Alexandersson, Oskar Nyholm. International Conference on Software Engineering, 2007

565 citations recorded by the source corpus.

Subfields: Information Systems, Software

View Paper: https://www.semanticscholar.org/paper/0d459e3be20f7f529bc0d92d42fa63e60fc1e1ba

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Information Systems

1. Sunghun Kim
2. Hongyu Zhang
3. David Lo
4. Kim Herzig
5. Andreas Zeller
6. Rongxin Wu
7. Tim Menzies
8. Thomas Zimmermann

In Software

1. Sunghun Kim
2. Kim Herzig
3. Andreas Zeller
4. David Lo
5. Tim Menzies
6. Hongyu Zhang
7. Sascha Just
8. Rongxin Wu

Citations in the corpus

• Improving bug triage with bug tossing graphs (2009, methodology): “To detect duplicates, several approaches use natural language processing techniques on the bug description [18, 23], sometimes combined with runtime traces [27].”
• ReLink: recovering links between bugs and changes (2011, background): “Runeson et al. [26] and Wang et al. [30] applied IR techniques to compare text similarity between two bug reports, thereby identifying potentially duplicate bug reports.”
• A discriminative model approach for accurate duplicate bug report retrieval (2010, methodology): “As mentioned in [17], duplicate reports can be divided into two categories.”
• What distinguishes great software engineers? (2019, background): “Bug triaging, examined by many researchers (Anvik and Murphy 2007; Jeong et al. 2009; Podgurski et al. 2003; Runeson et al. 2007; Bertram et al. 2010) is effectively a decision-making process.”
• It's not a bug, it's a feature: How misclassification impacts bug prediction (2013, background): “It is possible to automatically detect bug report duplicates [17], [18] that, when combined, might fill information gaps that prevent bug report fixes.”
• Predicting the severity of a reported bug (2010, methodology): “used text similarity techniques to help automate the detection of duplicate bug reports by comparing the similarities between bug reports [4].”
• RCLinker: Automated Linking of Issue Reports and Commits Leveraging Rich Contextual Information (2015, unknown): “There also a number of techniques that infer missing links between duplicated bug reports [52], [59], [26], [55], [54], [58], [48], [1].”
• How to effectively use topic models for software engineering tasks? An approach based on Genetic Algorithms (2013, unknown): “A number of approaches using LSI and LDA have been proposed to support software engineering tasks: feature location [4], change impact analysis [5], bug localization [6], clone detection [7], traceability link recovery [8], [9], expert developer recommendation [10], code measurement [11], [12], arti”
• Towards more accurate retrieval of duplicate bug reports (2011, methodology): “Consider for example Mozilla; in 2005, it was reported that “everyday almost 300 bugs appear that need triaging.”
• Automated severity assessment of software defect reports (2008, methodology): “[14], who proposed the recall rate as evaluation for their approach, also used in [18].”