A Meta-Model for Test Case Reduction by Reducing Cyclomatic Complexity in Regression Testing

Farooq, Ummer; Abubakar, .; Aqeel, Anas Bin

doi:10.1109/icrai54018.2021.9651395

Cited by 3 publications

(2 citation statements)

References 5 publications

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“…This approach, integrated into a software tool, performed exceptionally well in evaluations, demonstrating a high level of accuracy. The study [31] revealed that elevated CC results in prolonged durations during black box testing. The research advocates for mitigating this complexity by introducing a model aimed at reducing CC.…”

Section: Literature Reviewmentioning

confidence: 87%

Measuring Cyclomatic Complexity of Source Code Using Machine Learning

Odeh,

Odeh

et al. 2024

RIA

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High-performance, high-quality software is a fundamental goal for all developers. To achieve this, software needs to be built with exceptional quality, featuring a simplified structure, strong coherence, and minimized complexity. Cyclomatic Complexity (CC), critical software metric, quantifies the intricacy of a program's structure and control flow. Traditionally, CC measurement has relied on laborious manual techniques or conventional programming methods, both prone to errors and inefficiency. To overcome these limitations, we present a revolutionary approach that leverages a Multinomial Naive Bayes (MNB) machine learning (ML) algorithm for automated CC measurement. This innovative method offers a more accurate, efficient, and reliable means of software complexity evaluation. Our study utilizes a vast dataset of 3,598 carefully curated programming samples from diverse programming projects across three popular languages: Java, Python, and C++. Training our MNB model on this comprehensive and diverse dataset yielded an outstanding overall accuracy of 97.3%, demonstrating the efficacy and reliability of our approach for CC measurement across different programming languages (PLs). This success can be attributed to the utilization of the NBM learning algorithm, known for its proficiency in classification tasks. Additionally, our study benefits from a larger and more diverse dataset compared to prior research, potentially contributing to the superior results. Our novel approach to CC measurement using machine learning holds significant promise for the development of more accurate and reliable code complexity assessment tools. These encouraging findings suggest that this approach has the potential to shape more effective software development practices in the future.

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Section: Literature Reviewmentioning

confidence: 87%

Measuring Cyclomatic Complexity of Source Code Using Machine Learning

Odeh,

Odeh

et al. 2024

RIA

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“…For example, the "radon" library [30] computes CycC by traversing the AST and increasing the CycC value when an AST node with particular properties is encountered. Known applications of the CycC metric include refactoring prediction [31], malware detection [32], software testing [33], and test case reduction [34]. CycC is widely used in linters of programming languages, allowing them to quickly provide feedback to a programmer if a function or a method becomes too complex and hard to understand and maintain.…”

Section: Related Workmentioning

confidence: 99%

A Rule-Based Algorithm and Its Specializations for Measuring the Complexity of Software in Educational Digital Environments

Gorchakov,

Demidova,

Sovietov

2024

Computers

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Modern software systems consist of many software components; the source code of modern software systems is hard to understand and maintain for new developers. Aiming to simplify the readability and understandability of source code, companies that specialize in software development adopt programming standards, software design patterns, and static analyzers with the aim of decreasing the complexity of software. Recent research introduced a number of code metrics allowing the numerical characterization of the maintainability of code snippets. Cyclomatic Complexity (CycC) is one widely used metric for measuring the complexity of software. The value of CycC is equal to the number of decision points in a program plus one. However, CycC does not take into account the nesting levels of the syntactic structures that break the linear control flow in a program. Aiming to resolve this, the Cognitive Complexity (CogC) metric was proposed as a successor to CycC. In this paper, we describe a rule-based algorithm and its specializations for measuring the complexity of programs. We express the CycC and CogC metrics by means of the described algorithm and propose a new complexity metric named Educational Complexity (EduC) for use in educational digital environments. EduC is at least as strict as CycC and CogC are and includes additional checks that are based on definition-use graph analysis of a program. We evaluate the CycC, CogC, and EduC metrics using the source code of programs submitted to a Digital Teaching Assistant (DTA) system that automates a university programming course. The obtained results confirm that EduC rejects more overcomplicated and difficult-to-understand programs in solving unique programming exercises generated by the DTA system when compared to CycC and CogC.

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Machine Learning Model for Measuring Cyclomatic Complexity of Source Code

Odeh,

Odeh

et al. 2023

2023 International Conference on Intelligent Computing, Communication, Networking and Services (ICCNS)

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A Meta-Model for Test Case Reduction by Reducing Cyclomatic Complexity in Regression Testing

Cited by 3 publications

References 5 publications

Measuring Cyclomatic Complexity of Source Code Using Machine Learning

Measuring Cyclomatic Complexity of Source Code Using Machine Learning

A Rule-Based Algorithm and Its Specializations for Measuring the Complexity of Software in Educational Digital Environments

Machine Learning Model for Measuring Cyclomatic Complexity of Source Code

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