Accuracy Comparison of Empirical Studies on Software Product Maintainability Prediction

Elmidaoui, Sara; Cheikhi, Laila; Idri, Ali

doi:10.1007/978-3-319-77712-2_3

Cited by 8 publications

(3 citation statements)

References 31 publications

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“…Description [22] Ex An empirical process applied under controlled conditions to evaluate the application of ML technique in access control systems.…”

Section: Empirical Approachesmentioning

confidence: 99%

Exploring the Role of Machine Learning in Enhancing Access Control Systems: A Comprehensive Review

Ait el hadj

2023

IJCDS

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Access control (AC) systems are crucial for safeguarding sensitive data and resources, yet the increasing complexity of dynamic environments has underscored the need for more accurate and efficient solutions. Therefore, there is a growing need to enhance the accuracy, efficiency, and decision-making capabilities of AC systems. Machine learning (ML) techniques offer promising solutions to address these challenges and automate access control processes. This paper conducts a systematic review of ML techniques in AC systems, involving a comprehensive analysis covering the identification and classification of ML models and their specific applications. Through a meticulous examination of 62 relevant studies published between 2000 and 2023, the review reveals a predominant focus on innovative solutions and adaptations to enhance access control decision-making. Comparative studies play a significant role in assessing different ML approaches, with a focus on identifying the most effective methods for addressing access control challenges. Notably, Attribute-Based Access Control (ABAC) and Role-Based Access Control (RBAC) emerge as the predominant models, while Support Vector Machine (SVM) tops the list as the most commonly employed ML technique, followed by Random Forest (RF) and Decision Tree (DT). The review evaluates the performance and effectiveness of ML models in AC systems, highlighting their strengths and limitations. Additionally, it addresses significant challenges in the field and identifies potential directions for future research. This systematic review provides valuable insights into the current state of ML-based access control systems, fostering further advancements in this vital domain.

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“…Description [22] Ex An empirical process applied under controlled conditions to evaluate the application of ML technique in access control systems.…”

Section: Empirical Approachesmentioning

confidence: 99%

Exploring the Role of Machine Learning in Enhancing Access Control Systems: A Comprehensive Review

Ait el hadj

2023

IJCDS

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“…Elmidaoui et al (2019) conducted a literature review of SPMP studies to examine and synthesize empirical findings about the prediction accuracy of the SPMP methodologies in current research. They conducted an automated search on nine different electronic databases, based on mapping and evaluation of SPMP research published between 2000 and 2018.…”

Section: Introductionmentioning

confidence: 99%

A review on soft computing approaches for predicting maintainability of software: State‐of‐the‐art, technical challenges, and future directions

Yenduri

Gadekallu

2023

Expert Systems

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The software is changing rapidly with the invention of advanced technologies and methodologies. The ability to rapidly and successfully upgrade software in response to changing business requirements is more vital than ever. For the long‐term management of software products, measuring software maintainability is crucial. The use of soft computing techniques for software maintainability prediction has shown immense promise in software maintenance process by providing accurate prediction of software maintainability. To better understand the role of soft computing techniques for software maintainability prediction, we aim to provide a systematic literature review of soft computing techniques for predicting software maintainability. Firstly, we provide a detailed overview of software maintainability. Following this, we explore the fundamentals of software maintainability and the reasons for adopting soft computing methodologies for predicting software maintainability. Later, we examine the soft computing approaches employed in the process of software maintainability prediction. Furthermore, we discuss the difficulties and potential solutions associated with the use of soft computing techniques in predicting maintainability of software. Finally, we conclude the review with some promising future directions to drive further research innovations and developments in this promising area. This systematic literature review provides a comprehensive overview of the soft computing strategies utilized for software maintainability for future researchers.

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“…Then a comparative analysis is conducted to detail all the result obtained. Unlike [11], [25] which discuss the use of prediction techniques and compared the proposed models. In this paper we focus on the way of how the maintainability was predicted at different levels.…”

Section: Introductionmentioning

confidence: 99%

Comparative Analysis of Object-Oriented Software Maintainability Prediction Models

Zighed

Bounour

Seriai

2018

Foundations of Computing and Decision Sciences

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Software maintainability is one of the most important aspects when evaluating the quality of a software product. It is defined as the ease with which the existing software can be modified. In the literature, several researchers have proposed a large number of models to measure and predict maintainability throughout different phases of the Software Development Life Cycle. However, only a few attempts have been made for conducting a comparative study of the existent proposed prediction models. In this paper, we present a detailed classification and conduct a comparative analysis of Object-Oriented software maintainability prediction models. Furthermore, we considered the aforementioned proposed models from three perspectives, which are architecture, design and code levels. To the best of our knowledge, such an analysis that comprises the three levels has not been conducted in previous research. Moreover, this study hints at certain fundamental basics concerning the way of how measure the maintainability knowing that at each level the maintainability will be measured differently. In addition, we will focus on the strengths and weaknesses of these models. Consequently, the comparative study yields that several statistical and machine learning techniques have been employed for software maintainability prediction at code level during the last decade, and each technique possesses its specific characteristic to develop an accurate prediction model. At the design level, the majority of the prediction models measured maintainability according to the characteristics of the quality models. Whereas at the architectural level, the techniques adopted are still limited and only a few of studies have been conducted in this regard.

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Accuracy Comparison of Empirical Studies on Software Product Maintainability Prediction

Cited by 8 publications

References 31 publications

Exploring the Role of Machine Learning in Enhancing Access Control Systems: A Comprehensive Review

Exploring the Role of Machine Learning in Enhancing Access Control Systems: A Comprehensive Review

A review on soft computing approaches for predicting maintainability of software: State‐of‐the‐art, technical challenges, and future directions

Comparative Analysis of Object-Oriented Software Maintainability Prediction Models

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