Enhancing Software Feature Extraction Results Using Sentiment Analysis to Aid Requirements Reuse

Raharjana, Indra Kharisma; Aprillya, Via; Zaman, Badrus; Justitia, Army; Fauzi, Shukor Sanim Mohd

doi:10.3390/computers10030036

Cited by 8 publications

(3 citation statements)

References 31 publications

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“…App developers should also respond to feedback to help solve problems complained about by users. Negative feedback can also be a lesson to improve the app [55].…”

Section: Practical Implicationsmentioning

confidence: 99%

Influences of Social Learning on Customer’s Intention to Download an Application from a Start-up Company

Justitia¹,

Ambarrina²,

Raharjana³

et al. 2022

TEM Journal

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We examine the power of social learning on customers' intention to download an application (app) from a start-up company, usually a small business. This method is often referred to as social learning. In this case, the intention to download is influenced by the information learned from online groups and forums, product reviews, as well as wordof-mouth. In this study, the model is approached to examine customer behavior involving three social learning platforms; and how this can influence the intention to download an app from start-up companies.

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“…App developers should also respond to feedback to help solve problems complained about by users. Negative feedback can also be a lesson to improve the app [55].…”

Section: Practical Implicationsmentioning

confidence: 99%

Influences of Social Learning on Customer’s Intention to Download an Application from a Start-up Company

Justitia¹,

Ambarrina²,

Raharjana³

et al. 2022

TEM Journal

View full text Add to dashboard Cite

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“…Raharjana et al [13] provide an update from previous research on feature extraction and sentiment analysis with similarity measures from user reviews for reuse requirements. The extracted features are clustered based on polarity, subjectivity, and similarity values.…”

Section: Previous Researchesmentioning

confidence: 99%

A Semantic Comparison of Feature Requirements Extraction Methods

Manek

Septiyanto

Nugroho

2021

JTS

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Requirement engineering is an essential part of software development. The initial process in software development is to determine the needs of the stakeholders. To convert stakeholder needs into features of the system to be developed takes a long time, so it is a challenge for researchers to be able to extract features automatically based on the description of the needs of stakeholders. Previous research has also implemented feature extraction using user reviews on applications that public users have used. The feature extraction results will be used for feature development in future updated versions. The extraction process can use several proven methods to provide results that match the needs of the stakeholders in the system. This study compared the automatic feature extraction method using Natural Language Processing (NLP) with Hierarchical Pattern Recognition (HPR) on the dataset requirements and user reviews. Performance evaluation was conducted to test feature extraction results using Accuracy, precision, recall, and F-measure. The study results show that each method has advantages when implemented on both datasets. The NLP method excels in classifying the NL Requirement dataset. The HPR method has its advantages in extracting user review data.

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“…There are several benefits of implementing automated testing, such as faster execution than manual testing, more time for the testers, and the ability for the developers to create automated tests that often use the same programming language as the software product [16]. This also means that using generator tools for automated test cases could accelerate software development [17], [18], [19].…”

Section: Introductionmentioning

confidence: 99%

Conversion of User Story Scenarios to Python-Based Selenium Source Code for Automated Testing

et al. 2023

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A user story is an artifact in software development processes that can be used for automated test cases. This study proposes a terminal-based program for converting user stories into source codes for automated testing. We also propose a modified user story format that can be directly converted into functional Python-based Selenium source codes. We then implement the program by creating user stories for several local Django projects and using the converted codes. The implementation results show that the code from the program has a reliability rate of 72% for successfully executing it as the users' intentions.

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Enhancing Software Feature Extraction Results Using Sentiment Analysis to Aid Requirements Reuse

Cited by 8 publications

References 31 publications

Influences of Social Learning on Customer’s Intention to Download an Application from a Start-up Company

Influences of Social Learning on Customer’s Intention to Download an Application from a Start-up Company

A Semantic Comparison of Feature Requirements Extraction Methods

Conversion of User Story Scenarios to Python-Based Selenium Source Code for Automated Testing

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