Automatic Extraction of Design Decisions from Issue Management Systems: A Machine Learning Based Approach

Bhat, M. Seetharama; Shumaiev, Klym; Biesdorf, Andreas; Hohenstein, Uwe; Matthes, Florian

doi:10.1007/978-3-319-65831-5_10

Cited by 38 publications

(34 citation statements)

References 26 publications

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“…The studies of Sharma et al [22], Bhat et al [3], Brennan et al [7], Nassif et al [17] and Viviani et al [31] are similar to what is proposed to answer our RQ2. They identified knowledge in textual data from developer communication, through a classification technique.…”

Section: Related Worksupporting

confidence: 82%

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Reusing software engineering knowledge from developer communication

Silva

2020

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

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Section: Related Worksupporting

confidence: 82%

“…We used Gitter's search based on tags, names and description of chat rooms to identify chat rooms. The list of search terms is available online 3 . We selected all chat rooms except for non-English chat rooms, chat rooms without messages messages from bots, or chat rooms with fewer than four users.…”

Section: Exploring Knowledge In Developer Communicationmentioning

confidence: 99%

Reusing software engineering knowledge from developer communication

Silva

2020

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

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“…With regard to DD, the work of Bhat et al [77] identifies DD from the issue management systems that manage the software architectures. This work has improved by Bhat et al [78] supporting the DD-making process in terms of quality criteria and clustering the DD using the k-means algorithm.…”

Section: Related Workmentioning

confidence: 99%

Classifying Model-View-Controller Software Applications Using Self-Organizing Maps

2021

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The new era of information and the needs of our society require continuous change in software and technology. Changes are produced very quickly and software systems require evolving at the same velocity, which implies that the decision-making process of software architectures should be (semi-)automated to satisfy changing needs and to avoid wrong decisions. This issue is critical since suboptimal architecture design decisions may lead to high cost and poor software quality. Therefore, systematic and (semi-)automated mechanisms that help software architects during the decision-making process are required. Architectural patterns are one of the most important features of software applications, but the same pattern can be implemented in different ways, leaving to results of different quality. When an application requires to evolve, knowledge extracted from similar applications is useful for driving decisions, since quality pattern implementations can be reproduced in similar applications to improve specific quality attributes. Therefore, clustering methods are especially suitable for classifying similar pattern implementations. In this paper, we apply a novel unsupervised clustering technique, based on the well-known artificial neural network model Self-Organizing Maps, to classify Model-View-Controller (MVC) pattern from a quality point of view. Software quality is analyzed by 24 metrics organized into the categories of Count/Size, Maintainability, Duplications, Complexity, and Design Quality. The main goal of this work is twofold: to identify the quality features that establish the similarity of MVC applications without software architect bias, and to classify MVC applications by means of Self-Organizing Maps based on quality metrics. To that end, this work performs an exploratory study by conducting two analyses with a dataset of 87 Java MVC applications characterized by the 24 metrics and two attributes that describe the technology dimension of the application. The stated findings provide a knowledge base that can help in the decision-making process for the architecture of Java MVC applications.

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“…criteria, decisions, issues, alternatives and justification. Also, Bhat et al [34] expedite the issue tracking system to automatically identify and classify architectural design decisions using machine learning algorithms. Kanchev et al [35] extracted a large number of user comments from the user forum, then using high-level query language to identify requirement-related information for a system analyst to future decision making.…”

Section: Mining Rationale In Software and Requirements Engineeringmentioning

confidence: 99%

Requirements knowledge acquisition from online user forums

2020

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Online discussion forums can be used for reflecting on the overall user experience of a system. If a user forum is well-structured, it can be a valuable source of requirements-related information, which can potentially be accommodated in the requirements engineering process to enhance the current and future software. However, presently, there are limited approaches for extracting such requirements-related information from the relevant community forums. To fill this gap, this study proposes an automated approach, which automatically identifies requirements information using natural language processing and machine learning. For this purpose, the authors analysed 3319 user comments collected from the seven discussion topics in the Reddit forum. Then, using a content analysis approach, they studied how frequently end-users submit such information across each discussion topic. Also, they developed an automated approach that identifies key stakeholders, who frequently contribute his rationales in the forum discussion. Further, they employed different machine learning algorithms to classify user comments into rationale elements of different types. The authors' results show that online forums, such as Reddit, can be a rich source of requirements elicitation. Also, machine learning is a promising tool to detect user's rationale and identify different kinds of requirements modelling elements.

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Automatic Extraction of Design Decisions from Issue Management Systems: A Machine Learning Based Approach

Cited by 38 publications

References 26 publications

Reusing software engineering knowledge from developer communication

Reusing software engineering knowledge from developer communication

Classifying Model-View-Controller Software Applications Using Self-Organizing Maps

Requirements knowledge acquisition from online user forums

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