Reformulating Queries for Duplicate Bug Report Detection

Chaparro, Oscar; Florez, Juan Manuel; Singh, Unnati; Marcus, Andrian

doi:10.1109/saner.2019.8667985

Cited by 28 publications

(9 citation statements)

References 61 publications

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“…Unfortunately, many existing tools for detecting the duplicate bugs might not be mature enough for practical use. In particular, they simply rely on textual features [17,35], meta data from bug reports (e.g., products, components) [9] or execution traces [37] for detecting the duplicate bugs, and as a consequence, might fail to detect the complex duplicate bugs that have different symptoms but share the same root causes. Therefore, intelligent tools or techniques are warranted that can accurately detect the duplicate bugs during their report submission and thus can save the wasted efforts in failed reproduction.…”

Section: Rq 4 : How Do the Professional Developers Deal With Nonrepro...mentioning

confidence: 99%

Why are Some Bugs Non-Reproducible? : –An Empirical Investigation using Data Fusion–

Rahman

Khomh

Castelluccio³

2020

2020 IEEE International Conference on Software Maintenance and Evolution (ICSME)

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Software developers attempt to reproduce software bugs to understand their erroneous behaviours and to fix them. Unfortunately, they often fail to reproduce (or fix) them, which leads to faulty, unreliable software systems. However, to date, only a little research has been done to better understand what makes the software bugs non-reproducible. In this paper, we conduct a multimodal study to better understand the non-reproducibility of software bugs. First, we perform an empirical study using 576 non-reproducible bug reports from two popular software systems (Firefox, Eclipse) and identify 11 key factors that might lead a reported bug to non-reproducibility. Second, we conduct a user study involving 13 professional developers where we investigate how the developers cope with non-reproducible bugs. We found that they either close these bugs or solicit for further information, which involves long deliberations and counter-productive manual searches. Third, we offer several actionable insights on how to avoid non-reproducibility (e.g., false-positive bug report detector) and improve reproducibility of the reported bugs (e.g., sandbox for bug reproduction) by combining our analyses from multiple studies (e.g., empirical study, developer study).

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Section: Rq 4 : How Do the Professional Developers Deal With Nonrepro...mentioning

confidence: 99%

Why are Some Bugs Non-Reproducible? : –An Empirical Investigation using Data Fusion–

Rahman

Khomh

Castelluccio³

2020

2020 IEEE International Conference on Software Maintenance and Evolution (ICSME)

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“…Reports. Many research projects have focused on detecting duplicate textual bug reports [28,29,35,36,52,54,55,64,70,72,74,82,84,86,87,89,90,[93][94][95][96][97][99][100][101]107]. Similar to Ta n g o , most of the proposed techniques return a ranked list of duplicate candidates [35,63].…”

Section: Detection Of Duplicate Textual Bugmentioning

confidence: 99%

It Takes Two to Tango: Combining Visual and Textual Information for Detecting Duplicate Video-Based Bug Reports

Cooper

Bernal-Cárdenas

Chaparro

et al. 2021

2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE)

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When a bug manifests in a user-facing application, it is likely to be exposed through the graphical user interface (GUI). Given the importance of visual information to the process of identifying and understanding such bugs, users are increasingly making use of screenshots and screen-recordings as a means to report issues to developers. However, when such information is reported en masse, such as during crowd-sourced testing, managing these artifacts can be a time-consuming process. As the reporting of screen-recordings in particular becomes more popular, developers are likely to face challenges related to manually identifying videos that depict duplicate bugs. Due to their graphical nature, screen-recordings present challenges for automated analysis that preclude the use of current duplicate bug report detection techniques. To overcome these challenges and aid developers in this task, this paper presents TA N G O , a duplicate detection technique that operates purely on video-based bug reports by leveraging both visual and textual information. TANGO combines tailored computer vision techniques, optical character recognition, and text retrieval. We evaluated multiple configurations of Ta n g o in a comprehensive empirical evaluation on 4,860 duplicate detection tasks that involved a total of 180 screenrecordings from six Android apps. Additionally, we conducted a user study investigating the effort required for developers to manually detect duplicate video-based bug reports and compared this to the effort required to use TA N G O . The results reveal that TA N G O 's optimal configuration is highly effective at detecting duplicate video-based bug reports, accurately ranking target duplicate videos in the top-2 returned results in 83% of the tasks. Additionally, our user study shows that, on average, TANGO can reduce developer effort by over 60%, illustrating its practicality.

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“…duplicate bug report detection [18], bug report quality assessment [15,19], and other tasks that rely on bug reports [23,31,43].…”

Section: Bee's Usage Scenario and Featuresmentioning

confidence: 99%

“…We compiled the bug reports used in our prior research [16][17][18][19], which amount to 5,067 reports from 35 different software systems (e.g., Eclipse, Firefox, Docker, WordPress Android, OpenJPA), spanning different domains (e.g., data storage, software development, machine learning, virtualization, web browsing) and types (e.g., desktop, web, mobile, libraries). The bug reports contain 116,084 sentences total (including the title), where the ones describing the OB, EB, and S2R are manually annotated to make up the ground truth.…”

Section: Datamentioning

confidence: 99%

“…Bee is meant to assist developers, by structuring the bug descriptions via automated identification and labeling of OB, EB, and S2R sentences, allowing them to quickly spot these elements. Bee is also meant to assist researchers through a public web API for OB, EB, and S2R identification, which they can use for investigating and automating tasks that are based on these elements, such as bug reproduction [31,43], test case generation [23], bug localization [16,17], duplicate bug report detection [18], and bug report quality assessment [15,19].…”

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confidence: 99%

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BEE: a tool for structuring and analyzing bug reports

Song

Chaparro

2020

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

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This paper introduces Bee, a tool that automatically analyzes userwritten bug reports and provides feedback to reporters and developers about the system's observed behavior (OB), expected behavior (EB), and the steps to reproduce the bug (S2R). Bee employs machine learning to (i) detect if an issue describes a bug, an enhancement, or a question; (ii) identify the structure of bug descriptions by automatically labeling the sentences that correspond to the OB, EB, or S2R; and (iii) detect when bug reports fail to provide these elements. Bee is integrated with GitHub and offers a public web API that researchers can use to investigate bug management tasks based on bug reports. We evaluated Bee's underlying models on more than 5k existing bug reports and found they can correctly detect OB, EB, and S2R sentences as well as missing information in bug reports. Bee is an open-source project that can be found at https://git.io/JfFnN. A screencast showing the full capabilities of Bee can be found at https://youtu.be/8pC48f_hClw. CCS CONCEPTS • Software and its engineering → Software maintenance tools.

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Reformulating Queries for Duplicate Bug Report Detection

Cited by 28 publications

References 61 publications

Why are Some Bugs Non-Reproducible? : –An Empirical Investigation using Data Fusion–

Why are Some Bugs Non-Reproducible? : –An Empirical Investigation using Data Fusion–

It Takes Two to Tango: Combining Visual and Textual Information for Detecting Duplicate Video-Based Bug Reports

BEE: a tool for structuring and analyzing bug reports

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