Automated Recommendation of Software Refactorings Based on Feature Requests

Nyamawe, Ally S.; Liu, Hui; Niu, Nan; Umer, Qasim; Niu, Zhendong

doi:10.1109/re.2019.00029

Cited by 20 publications

(21 citation statements)

References 41 publications

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“…Prioritization was supported in the studies [41,48,49,52,58,60,68,72,73,81,[87][88][89]. • Elicitation for change management is to elicit emerging requirements for software evolution, which has been supported in the studies [52,85,87,[97][98][99]102]. • Documentation of quality requirements has been enabled by two studies [88,89], by automatically specifying user stories or semi-formally written requirements in a product backlog, respectively.…”

Section: Discussionmentioning

confidence: 99%

“…Seven studies (10%) leveraged data from software repositories: the Apache OpenOffice issue tracking system [84], issues mined from the Android OS issue tracker [23], the natural language and features from the issue tracking system metadata of the four open-source projects [85], and GitHub [86], GitHub and JIRA issue tracker [87], and a data sink tank containing data from multiple software repositories (e.g., GitHub, JIRA, Jenkins, and SonarQube) [88,89] Software/app product descriptions Five studies (7%) used software product descriptions on Softpedia [90,91], app change logs [92,93], or app description page [94] Sensor readings…”

Section: Software Repositoriesmentioning

confidence: 99%

“…In their different study [79], open-source software mailing lists, and OpenOffice online discussions were used to identify relevant requirements information. Nyamawe et al [87] used commits from GitHub repository and feature requests from JIRA issue tracker, while Oriol et al [89] and Franch et al [88] considered heterogenous software repositories.…”

Section: Usage Datamentioning

confidence: 99%

See 2 more Smart Citations

Data-Driven Requirements Elicitation: A Systematic Literature Review

2021

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Requirements engineering has traditionally been stakeholder-driven. In addition to domain knowledge, widespread digitalization has led to the generation of vast amounts of data (Big Data) from heterogeneous digital sources such as the Internet of Things (IoT), mobile devices, and social networks. The digital transformation has spawned new opportunities to consider such data as potentially valuable sources of requirements, although they are not intentionally created for requirements elicitation. A challenge to data-driven requirements engineering concerns the lack of methods to facilitate seamless and autonomous requirements elicitation from such dynamic and unintended digital sources. There are numerous challenges in processing the data effectively to be fully exploited in organizations. This article, thus, reviews the current state-of-the-art approaches to data-driven requirements elicitation from dynamic data sources and identifies research gaps. We obtained 1848 hits when searching six electronic databases. Through a two-level screening and a complementary forward and backward reference search, 68 papers were selected for final analysis. The results reveal that the existing automated requirements elicitation primarily focuses on utilizing human-sourced data, especially online reviews, as requirements sources, and supervised machine learning for data processing. The outcomes of automated requirements elicitation often result in mere identification and classification of requirements-related information or identification of features, without eliciting requirements in a ready-to-use form. This article highlights the need for developing methods to leverage process-mediated and machine-generated data for requirements elicitation and addressing the issues related to variety, velocity, and volume of Big Data for the efficient and effective software development and evolution.

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Section: Discussionmentioning

confidence: 99%

Section: Software Repositoriesmentioning

confidence: 99%

Section: Usage Datamentioning

confidence: 99%

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Data-Driven Requirements Elicitation: A Systematic Literature Review

2021

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“…Additionally, Nyamawe et al (2018) indicated that selecting the best refactoring technique from some potential techniques to remove a design flaw is challenging for software developers. According to Nyamawe et al (2019), it is often challenging to determine which kind of refactoring technique should be applied. In other words, it is very challenging for software practitioners to select appropriate refactoring techniques to improve software quality (Al-Dallal & Abdin, 2018;Almogahed et al, 2018;Almogahed et al, 2019;Kaur & Singh, 2019).…”

Section: Introductionmentioning

confidence: 99%

Refactoring Techniques for Improving Software Quality: Practitioners’ Perspectives

Almogahed

Omar

2021

Journal of Information and Communication Technology

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Refactoring is a critical task in software maintenance and is commonly applied to improve system design or to cope with design defects. There are 68 different types of refactoring techniques and each technique has a particular purpose and effect. However, most prior studies have selected refactoring techniques based on their common use in academic research without obtaining evidence from the software industry. This is a shortcoming that points to the existence of a clear gap between academic research and the corresponding industry practices. Therefore, to bridge this gap, this study identified the most frequently used refactoring techniques, the commonly used programming language, and methods of applying refactoring techniques in the current practices of software refactoring among software practitioners in the industry, by using an online survey. The findings from the survey revealed the most used refactoring techniques, programming language, and the methods of applying the refactoring techniques. This study contributes toward the improvement of software development practices by adding empirical evidence on software refactoring used by software developers. The findings would be beneficial for researchers to develop reference models and software tools to guide the practitioners in using these refactoring techniques based on their effect on software quality attributes to improve the quality of the software systems as a whole.

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“…During software maintenance, programmers are asked to add a new feature, remove and/or modify existing ones. The process of adapting these features needs to modify the software systems to meet the required requirement, this process called refactoring [1]. Code refactoring plays an important role in enhancing software quality by evolving the internal structure without affecting the intended behavior [2].…”

Section: Introductionmentioning

confidence: 99%

Harnessing deep learning algorithms to predict software refactoring

2020

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During software maintenance, software systems need to be modified by adding or modifying source code. These changes are required to fix errors or adopt new requirements raised by stakeholders or market place. Identifying the targeted piece of code for refactoring purposes is considered a real challenge for software developers. The whole process of refactoring mainly relies on software developers' skills and intuition. In this paper, a deep learning algorithm is used to develop a refactoring prediction model for highlighting the classes that require refactoring. More specifically, the gated recurrent unit algorithm is used with proposed pre-processing steps for refactoring prediction at the class level. The effectiveness of the proposed model is evaluated using a very common dataset of 7 open source java projects. The experiments are conducted before and after balancing the dataset to investigate the influence of data sampling on the performance of the prediction model. The experimental analysis reveals a promising result in the field of code refactoring prediction.

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Automated Recommendation of Software Refactorings Based on Feature Requests

Cited by 20 publications

References 41 publications

Data-Driven Requirements Elicitation: A Systematic Literature Review

Data-Driven Requirements Elicitation: A Systematic Literature Review

Refactoring Techniques for Improving Software Quality: Practitioners’ Perspectives

Harnessing deep learning algorithms to predict software refactoring

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