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All Journal TELKOMNIKA (Telecommunication Computing Electronics and Control) Bulletin of Electrical Engineering and Informatics
Pathiah Abdul Samat
Universiti Putra Malaysia
Computer Science & IT Electrical & Electronics Engineering

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Requirements identification for distributed agile team communication using high level carotene Nor Hidayah Zainal Abidin; Pathiah Abdul Samat
Bulletin of Electrical Engineering and Informatics Vol 10, No 1: February 2021
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v10i1.2031

Abstract

Communication plays an important role to deliver the correct information. However, the communication became challenging especially for agile software teams, which are in geographical distributed. The problem arise when there are exchanging information using unstructured communication platform, misunderstanding on the information communicated and lack of documentation. The aim of this study is to propose a text classification technique for requirements identification in text messages. In this study, we adopted the cascade and cluster classification concept of Carotene that relies on the hash tag function. It classifies the text messages into requirements types instead of job title. This technique called as high-level carotene (HLC) technique that embedded into the tool to identify the functional requirement and non-functional requirements. The result shows that most of criterias evaluated have achieved more than 85% of effectiveness in identifying both of requirement in text messaging by using this technique.
The influence of machine learning on the predictive performance of cross-project defect prediction: empirical analysis Yahaya Zakariyau Bala; Pathiah Abdul Samat; Khaironi Yatim Sharif; Noridayu Manshor
TELKOMNIKA (Telecommunication Computing Electronics and Control) Vol 22, No 4: August 2024
Publisher : Universitas Ahmad Dahlan

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.12928/telkomnika.v22i4.25916

Abstract

This empirical investigation delves into the influence of machine learning (ML) algorithms in the realm of cross-project defect prediction, employing the AEEEEM dataset as a foundation. The primary objective is to discern the nuanced influences of various algorithms on predictive performance, with a specific focus on the F1 score metric as evaluation criterion. Four ML algorithms have been carefully assessed in this study: random forest (RF), support vector machines (SVM), k-nearest neighbors (KNN), and logistic regression (LR). The choice of these algorithms reflects their prevalence in software defect prediction literature and their diversity. Through rigorous experimentation and analysis, the investigation unveils compelling evidence affirming the superiority of RF over its counterparts. The F1 score utilized as evaluation metric, capturing the delicate balance between precision and recall, essential in defect prediction scenarios. The nuanced examination of algorithmic efficacy provides practical insights for developers and practitioners navigating the challenges of cross-project defect prediction. By leveraging the rich and diverse AEEEEM dataset, this study ensures a comprehensive exploration of algorithmic influences across varied software projects. The findings not only contribute to the academic discourse on defect prediction but also offer practical guidance for real-world application, emphasizing the pivotal role of RF as a tool in enhancing predictive accuracy and reliability.
Co-Authors Khaironi Yatim Sharif Nor Hidayah Zainal Abidin Noridayu Manshor Yahaya Zakariyau Bala