Bug Reports Evolution in Open Source Systems

Aljedaani, Wajdi; Javed, Yasir

doi:10.1007/978-3-319-78753-4_6

Cited by 8 publications

(5 citation statements)

References 16 publications

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“…. .>): TC1(C1) − {3.1, 14.2}, <(3, 10); (3,7), (1,6)>, CS(C1) − {4.7, 19.7}, < (3,13), (4,22), (4, 15)>, CC(C1) − {0.95, 4.8}, <(0.9, 7.1), (1, 5.5), (0.9, 4)> The maximum values were as follows: 91-165, 62-164, and 56-142 days for TC1, CS, and CC parameters, respectively (with minimum and Q1 values close to 0). The median and Q3 values were below the period of a single sprint, which confirmed the relatively good consistency of handling issues with the sprint plan.…”

Section: Issue Commenting Activitiesmentioning

confidence: 99%

“…The average number of comments per issue was about 2 (1-3, depending on issue priority), and there were maximally 17 comments. In the case of the Scrum C1 commercial project, the commentators constituted three groups, which, to some extent, overlapped with these issue reporters: Testers (6), Developers (10 out 15), Business Analysts (4), and the Product owner (1). They generated 547 , 689 (39-129), 234 (41-102), and 64 comments for three subsequent sprints, respectively.…”

Section: Issue Commenting Activitiesmentioning

confidence: 99%

“…Software project development and maintenance need tracing of relevant reports in software repositories, in particular those generated via issue tracking (e.g., Bugzilla, Jira, Mantis) and software version control systems (e.g., GitHub). This has been widely discussed in the literature, e.g., [1][2][3]. An issue-tracking system (ITS) manages the running of reports on recorded problems, their processing status and progress, and their final resolution (e.g., completed, fixed, rejected, not a problem, and duplicated).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Enhancing Software Project Monitoring with Multidimensional Data Repository Mining

Reszka,

Sosnowski,

Dobrzyński

2023

Electronics

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Software project development and maintenance activities have been reported in various repositories. The data contained in these repositories have been widely used in various studies on specific problems, e.g., predicting bug appearance, allocating issues to developers, and identifying duplicated issues. Developed analysis schemes are usually based on simplified data models while issue report details are neglected. Confronting this problem requires a deep and wide-ranging exploration of software repository contents adapted to their specificities, which differs significantly from classical data mining. This paper is targeted at three aspects: the structural and semantic exploration of repositories, deriving characteristic features in value and time perspectives, and defining the space of project monitoring goals. The considerations presented demonstrate a holistic image of the project development process, which is useful in the assessment of its efficiency and identification of imperfections. The original analysis introduced in this work was verified using open source and some commercial software project repositories.

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Section: Issue Commenting Activitiesmentioning

confidence: 99%

Section: Issue Commenting Activitiesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Enhancing Software Project Monitoring with Multidimensional Data Repository Mining

Reszka,

Sosnowski,

Dobrzyński

2023

Electronics

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“…Many researchers concluded that reviews and ratings posted by users on app store platforms could play an essential role in apps' evolution since most developers consider users' reviews when working on a new release [91,181,238,245,23,264,31,229]. Maalej et al [192] proposed to consider user input as the first means of requirements elicitation in software development.…”

Section: User Reviewsmentioning

confidence: 99%

“…There are many challenges with detecting accessibility related to user reviews. One of the most common ways of improving applications is by analyzing the feedback given by the users [91,23,181,31]. In many cases, accessibility user reviews for mobile applications are overlooked [113].…”

Section: Chapter Summarymentioning

confidence: 99%

Toward Leveraging Artificial Intelligence to Support the Identification of Accessibility Challenges

Aljedaani

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The goal of this thesis is to support the automated identification of accessibility in user reviews or bug reports, to help technology professionals prioritize their handling, and, thus, to create more inclusive apps. Particularly, we propose a model that takes as input accessibility user reviews or bug reports and learns their keyword-based features to make a classification decision, for a given review, on whether it is about accessibility or not. Our empirically driven study follows a mixture of qualitative and quantitative methods. We introduced models that can accurately identify accessibility reviews and bug reports and automate detecting them. Our models can automatically classify app reviews and bug reports as accessibility-related or not so developers can easily detect accessibility issues with their products and improve them to more accessible and inclusive apps utilizing the users' input. Our goal is to create a sustainable change by including a model in the developer's software maintenance pipeline and raising awareness of existing errors that hinder the accessibility of mobile apps, which is a pressing need. In light of our findings from the Blackboard case study, Blackboard and the course material are not easily accessible to deaf students and hard of hearing. Thus, deaf students find that learning is extremely stressful during the pandemic.

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ProRE: An ACO- based programmer recommendation model to precisely manage software bugs

Kukkar

Lilhore

Frnda

et al. 2023

Journal of King Saud University - Computer and Information Scie

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Bug Reports Evolution in Open Source Systems

Cited by 8 publications

References 16 publications

Enhancing Software Project Monitoring with Multidimensional Data Repository Mining

Enhancing Software Project Monitoring with Multidimensional Data Repository Mining

Toward Leveraging Artificial Intelligence to Support the Identification of Accessibility Challenges

ProRE: An ACO- based programmer recommendation model to precisely manage software bugs

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