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<p>Understanding the process of execution of code is essential in many software engineering activities, such as debugging, optimization, security analysis, and comprehending complicated systems. The paper provides a thorough investigation of approaches for analyzing the execution flow of code<b>.</b> Software testing is a vital step in the development phase of software, in which problems are discovered to ensure that the software system is reliable before it is released to consumers. Achieving thorough test coverage is critical for improving testing effectiveness. This work gives a thorough investigation into the execution-oriented code-based testing coverage reliability growth model for software. The growth model is a useful tool for anticipating software reliability in the software testing lifecycle phase. The model incorporates different testing coverage functions grounded on three types of execution orientation of code. The model’s distinctiveness is demonstrated by verifying it on a software fault dataset and then comparing the estimation findings to the Inflection S Shaped Model. The research also uses Genetic Algorithms to optimize the release planning process by repeatedly creating and assessing release plans based on execution-based coverage modeling, resulting in the best-fit release timing. The release optimization problem aims to reduce testing costs while achieving a specified percentage of fault elimination.</p>

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Reliability assessment of coverage execution oriented software growth model and genetic algorithm driven release planning

  • 期刊论文

  • 发表日期：

    2025年1月6日

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  • Gurjeet Kaur,
  • Abhishek Tandon,
  • Rubina Mittal,
  • Atul Singh

摘要

Understanding the process of execution of code is essential in many software engineering activities, such as debugging, optimization, security analysis, and comprehending complicated systems. The paper provides a thorough investigation of approaches for analyzing the execution flow of code. Software testing is a vital step in the development phase of software, in which problems are discovered to ensure that the software system is reliable before it is released to consumers. Achieving thorough test coverage is critical for improving testing effectiveness. This work gives a thorough investigation into the execution-oriented code-based testing coverage reliability growth model for software. The growth model is a useful tool for anticipating software reliability in the software testing lifecycle phase. The model incorporates different testing coverage functions grounded on three types of execution orientation of code. The model’s distinctiveness is demonstrated by verifying it on a software fault dataset and then comparing the estimation findings to the Inflection S Shaped Model. The research also uses Genetic Algorithms to optimize the release planning process by repeatedly creating and assessing release plans based on execution-based coverage modeling, resulting in the best-fit release timing. The release optimization problem aims to reduce testing costs while achieving a specified percentage of fault elimination.