Linguistic analysis of bug report titles with respect to the dimension of bug importance

Sureka, Ashish; Indukuri, Kishore Varma

doi:10.1145/1754288.1754297

Cited by 10 publications

(7 citation statements)

References 7 publications

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“…Issue titles have been proven to be important features for the detection of duplicated bugs [26] and previous research [28], [38] demonstrated that issue titles have characteristics or features that can be exploited to assign the correct severity level to a bug.…”

Section: Mining Of Recurrent Patterns In Similar Issuesmentioning

confidence: 99%

Exploiting Natural Language Structures in Software Informal Documentation

Sorbo

Panichella

Visaggio

et al. 2021

IIEEE Trans. Software Eng.

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Communication means, such as issue trackers, mailing lists, Q&A forums, and app reviews, are premier means of collaboration among developers, and between developers and end-users. Analyzing such sources of information is crucial to build recommenders for developers, for example suggesting experts, re-documenting source code, or transforming user feedback in maintenance and evolution strategies for developers. To ease this analysis, in previous work we proposed DECA (Development Emails Content Analyzer), a tool based on Natural Language Parsing that classifies with high precision development emails' fragments according to their purpose. However, DECA has to be trained through a manual tagging of relevant patterns, which is often effort-intensive, error-prone and requires specific expertise in natural language parsing. In this paper, we first show, with a study involving Master's and Ph.D. students, the extent to which producing rules for identifying such patterns requires effort, depending on the nature and complexity of patterns. Then, we propose an approach, named NEON (Nlp-based softwarE dOcumentation aNalyzer), that automatically mines such rules, minimizing the manual effort. We assess the performances of NEON in the analysis and classification of mobile app reviews, developers discussions, and issues. NEON simplifies the patterns' identification and rules' definition processes, allowing a savings of more than 70% of the time otherwise spent on performing such activities manually. Results also show that NEON-generated rules are close to the manually identified ones, achieving comparable recall.

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Section: Mining Of Recurrent Patterns In Similar Issuesmentioning

confidence: 99%

Exploiting Natural Language Structures in Software Informal Documentation

Sorbo

Panichella

Visaggio

et al. 2021

IIEEE Trans. Software Eng.

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show abstract

“…, which used log entropy and TF entropy. Additionally, all reasoning algorithms implement learning techniques, as follows: SVM , decision tree , k‐nearest neighbor , Naive Bayes , and Naive Bayes Multinomial .…”

Section: Challenges: Improving the Management Of Change Requestmentioning

confidence: 99%

“…Nevertheless, it does not necessarily indicate the importance that each topic has in CR management. Additionally, the studies [12,18,22,24,56,81,83,88,91,117,121,122,123,124] [ 69,70,71,72,73,74,75,76 ] that addressed more than one topic were classified repeatedly in each topic, that is, since Hosseini et al [19] investigated the topics of CR assignment and time to fix, their study is counted under both two topics.…”

Section: Classification Schemementioning

confidence: 99%

“…For example, when a new CR is opened, existing CRs, already prioritized, can be queried by using textual information from the new CR, which will retrieve a set of similar CRs. Then, the priority of the new CR is calculated on the basis of the priority of the retrieved CRs [70,[72][73][74][75][76]. Table VII shows the approaches classified according to their IR model characteristics.…”

Section: Analysis Of Duplicate Change Requestsmentioning

confidence: 99%

“…For the vector space representation, binary and term count was used in [74] and TF-IDF in all other approaches, except by Gegick et al [70], which used log entropy and TF entropy. Additionally, all reasoning algorithms implement learning techniques, as follows: SVM [69,72,74,76], decision tree [69,76], k-nearest neighbor [69,74], Naive Bayes [69,[72][73][74], and Naive Bayes Multinomial [69].…”

Section: Analysis Of Duplicate Change Requestsmentioning

confidence: 99%

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Challenges and opportunities for software change request repositories: a systematic mapping study

Cavalcanti

Neto

Machado

et al. 2013

J Software Evolu Process

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Software maintenance starts as soon as the first artifacts are delivered and is essential for the success of the software. However, keeping maintenance activities and their related artifacts on track comes at a high cost. In this respect, change request (CR) repositories are fundamental in software maintenance. They facilitate the management of CRs and are also the central point to coordinate activities and communication among stakeholders. However, the benefits of CR repositories do not come without issues, and commonly occurring ones should be dealt with, such as the following: duplicate CRs, the large number of CRs to assign, or poorly described CRs. Such issues have led researchers to an increased interest in investigating CR repositories, by considering different aspects of software development and CR management. In this paper, we performed a systematic mapping study to characterize this research field. We analyzed 142 studies, which we classified in two ways. First, we classified the studies into different topics and grouped them into two dimensions: challenges and opportunities. Second, the challenge topics were classified in accordance with an existing taxonomy for information retrieval models. In addition, we investigated tools and services for CR management, to understand whether and how they addressed the topics identified.

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