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  • Open Access

    ARTICLE

    AI-Driven Prioritization and Filtering of Windows Artifacts for Enhanced Digital Forensics

    Juhwan Kim, Baehoon Son, Jihyeon Yu, Joobeom Yun*

    CMC-Computers, Materials & Continua, Vol.81, No.2, pp. 3371-3393, 2024, DOI:10.32604/cmc.2024.057234 - 18 November 2024

    Abstract Digital forensics aims to uncover evidence of cybercrimes within compromised systems. These cybercrimes are often perpetrated through the deployment of malware, which inevitably leaves discernible traces within the compromised systems. Forensic analysts are tasked with extracting and subsequently analyzing data, termed as artifacts, from these systems to gather evidence. Therefore, forensic analysts must sift through extensive datasets to isolate pertinent evidence. However, manually identifying suspicious traces among numerous artifacts is time-consuming and labor-intensive. Previous studies addressed such inefficiencies by integrating artificial intelligence (AI) technologies into digital forensics. Despite the efforts in previous studies, artifacts were… More >

  • Open Access

    ARTICLE

    Harnessing ML and GIS for Seismic Vulnerability Assessment and Risk Prioritization

    Shalu1, Twinkle Acharya1, Dhwanilnath Gharekhan1,*, Dipak Samal2

    Revue Internationale de Géomatique, Vol.33, pp. 111-134, 2024, DOI:10.32604/rig.2024.051788 - 15 May 2024

    Abstract Seismic vulnerability modeling plays a crucial role in seismic risk assessment, aiding decision-makers in pinpointing areas and structures most prone to earthquake damage. While machine learning (ML) algorithms and Geographic Information Systems (GIS) have emerged as promising tools for seismic vulnerability modeling, there remains a notable gap in comprehensive geospatial studies focused on India. Previous studies in seismic vulnerability modeling have primarily focused on specific regions or countries, often overlooking the unique challenges and characteristics of India. In this study, we introduce a novel approach to seismic vulnerability modeling, leveraging ML and GIS to address… More >

  • Open Access

    ARTICLE

    C-CORE: Clustering by Code Representation to Prioritize Test Cases in Compiler Testing

    Wei Zhou1, Xincong Jiang2,*, Chuan Qin2

    CMES-Computer Modeling in Engineering & Sciences, Vol.139, No.2, pp. 2069-2093, 2024, DOI:10.32604/cmes.2023.043248 - 29 January 2024

    Abstract Edge devices, due to their limited computational and storage resources, often require the use of compilers for program optimization. Therefore, ensuring the security and reliability of these compilers is of paramount importance in the emerging field of edge AI. One widely used testing method for this purpose is fuzz testing, which detects bugs by inputting random test cases into the target program. However, this process consumes significant time and resources. To improve the efficiency of compiler fuzz testing, it is common practice to utilize test case prioritization techniques. Some researchers use machine learning to predict… More >

  • Open Access

    ARTICLE

    Security Test Case Prioritization through Ant Colony Optimization Algorithm

    Abdulaziz Attaallah1, Khalil al-Sulbi2, Areej Alasiry3, Mehrez Marzougui3, Mohd Waris Khan4,*, Mohd Faizan4, Alka Agrawal5, Dhirendra Pandey5

    Computer Systems Science and Engineering, Vol.47, No.3, pp. 3165-3195, 2023, DOI:10.32604/csse.2023.040259 - 09 November 2023

    Abstract Security testing is a critical concern for organizations worldwide due to the potential financial setbacks and damage to reputation caused by insecure software systems. One of the challenges in software security testing is test case prioritization, which aims to reduce redundancy in fault occurrences when executing test suites. By effectively applying test case prioritization, both the time and cost required for developing secure software can be reduced. This paper proposes a test case prioritization technique based on the Ant Colony Optimization (ACO) algorithm, a metaheuristic approach. The performance of the ACO-based technique is evaluated using More >

  • Open Access

    ARTICLE

    Bug Prioritization Using Average One Dependence Estimator

    Kashif Saleem1, Rashid Naseem1, Khalil Khan1,2, Siraj Muhammad3, Ikram Syed4,*, Jaehyuk Choi4,*

    Intelligent Automation & Soft Computing, Vol.36, No.3, pp. 3517-3533, 2023, DOI:10.32604/iasc.2023.036356 - 15 March 2023

    Abstract Automation software need to be continuously updated by addressing software bugs contained in their repositories. However, bugs have different levels of importance; hence, it is essential to prioritize bug reports based on their severity and importance. Manually managing the deluge of incoming bug reports faces time and resource constraints from the development team and delays the resolution of critical bugs. Therefore, bug report prioritization is vital. This study proposes a new model for bug prioritization based on average one dependence estimator; it prioritizes bug reports based on severity, which is determined by the number of More >

  • Open Access

    ARTICLE

    Fault Coverage-Based Test Case Prioritization and Selection Using African Buffalo Optimization

    Shweta Singhal1, Nishtha Jatana2, Ahmad F Subahi3, Charu Gupta4,*, Osamah Ibrahim Khalaf5, Youseef Alotaibi6

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6755-6774, 2023, DOI:10.32604/cmc.2023.032308 - 28 December 2022

    Abstract Software needs modifications and requires revisions regularly. Owing to these revisions, retesting software becomes essential to ensure that the enhancements made, have not affected its bug-free functioning. The time and cost incurred in this process, need to be reduced by the method of test case selection and prioritization. It is observed that many nature-inspired techniques are applied in this area. African Buffalo Optimization is one such approach, applied to regression test selection and prioritization. In this paper, the proposed work explains and proves the applicability of the African Buffalo Optimization approach to test case selection… More >

  • Open Access

    ARTICLE

    Test Case Prioritization in Unit and Integration Testing: A Shuffled-Frog-Leaping Approach

    Atulya Gupta*, Rajendra Prasad Mahapatra

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 5369-5387, 2023, DOI:10.32604/cmc.2023.031261 - 28 December 2022

    Abstract Both unit and integration testing are incredibly crucial for almost any software application because each of them operates a distinct process to examine the product. Due to resource constraints, when software is subjected to modifications, the drastic increase in the count of test cases forces the testers to opt for a test optimization strategy. One such strategy is test case prioritization (TCP). Existing works have propounded various methodologies that re-order the system-level test cases intending to boost either the fault detection capabilities or the coverage efficacy at the earliest. Nonetheless, singularity in objective functions and… More >

  • Open Access

    ARTICLE

    An Optimized Test Case Minimization Technique Using Genetic Algorithm for Regression Testing

    Rubab Sheikh1, Muhammad Imran Babar2,*, Rawish Butt3, Abdelzahir Abdelmaboud4, Taiseer Abdalla Elfadil Eisa4

    CMC-Computers, Materials & Continua, Vol.74, No.3, pp. 6789-6806, 2023, DOI:10.32604/cmc.2023.028625 - 28 December 2022

    Abstract Regression testing is a widely used approach to confirm the correct functionality of the software in incremental development. The use of test cases makes it easier to test the ripple effect of changed requirements. Rigorous testing may help in meeting the quality criteria that is based on the conformance to the requirements as given by the intended stakeholders. However, a minimized and prioritized set of test cases may reduce the efforts and time required for testing while focusing on the timely delivery of the software application. In this research, a technique named TestReduce has been presented More >

  • Open Access

    ARTICLE

    A Load-Fairness Prioritization-Based Matching Technique for Cloud Task Scheduling and Resource Allocation

    Abdulaziz Alhubaishy1,*, Abdulmajeed Aljuhani2

    Computer Systems Science and Engineering, Vol.45, No.3, pp. 2461-2481, 2023, DOI:10.32604/csse.2023.032217 - 21 December 2022

    Abstract In a cloud environment, consumers search for the best service provider that accomplishes the required tasks based on a set of criteria such as completion time and cost. On the other hand, Cloud Service Providers (CSPs) seek to maximize their profits by attracting and serving more consumers based on their resource capabilities. The literature has discussed the problem by considering either consumers’ needs or CSPs’ capabilities. A problem resides in the lack of explicit models that combine preferences of consumers with the capabilities of CSPs to provide a unified process for resource allocation and task… More >

  • Open Access

    ARTICLE

    Value-Based Test Case Prioritization for Regression Testing Using Genetic Algorithms

    Farrukh Shahzad Ahmed, Awais Majeed, Tamim Ahmed Khan*

    CMC-Computers, Materials & Continua, Vol.74, No.1, pp. 2211-2238, 2023, DOI:10.32604/cmc.2023.032664 - 22 September 2022

    Abstract Test Case Prioritization (TCP) techniques perform better than other regression test optimization techniques including Test Suite Reduction (TSR) and Test Case Selection (TCS). Many TCP techniques are available, and their performance is usually measured through a metric Average Percentage of Fault Detection (APFD). This metric is value-neutral because it only works well when all test cases have the same cost, and all faults have the same severity. Using APFD for performance evaluation of test case orders where test cases cost or faults severity varies is prone to produce false results. Therefore, using the right metric… More >

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