Quality Assurance Practices in Agile Methodology

• URL: http://arxiv.org/abs/2411.05134v1
• Date: Thu, 07 Nov 2024 19:45:40 GMT
• Title: Quality Assurance Practices in Agile Methodology
• Authors: Almustapha A. Wakili, Lawan Nasir Alhassan, Abubakar Kamagata,
• Abstract summary: The complexity of software is increasing day by day the requirement and need for a verity of softwareproducts increases. The practice of applying software metrics to the development process and to asoftware product is a critical task and crucial enough that requires study and discipline.
• Score: 0.0
• License:
• Abstract: The complexity of software is increasing day by day the requirement and need for a verity of softwareproducts increases, this necessitates the provision of a strong tool that will make a balance betweenproduction and quality. The practice of applying software metrics to the development process and to asoftware product is a critical task and crucial enough that requires study and discipline and whichbrings knowledge of the status of the process and/or product of software in regards to the goals toachieve, this discipline is known as quality assurance which is the key factor behind the success ofevery software engineering project, the quality assurance activities are what result in the qualitativeproduct as well as the process in both conventional software development methodology and agilemethodology. However, agile methodology is now becoming one of the dominant method adopted bymost of the software industries because it allows developing of software with very limited requirementand supports rapid changes in the requirement, the method may produce the product very fast but wemight not guarantee the quality of the product unless we apply the SQA activities to the process. Thisresearch paper aimed to study the quality assurance activities practice in agile software developmentmethodology, investigate the common problems and key drivers of quality in agile, and propose asolution to improve the practice of SQA in agile methodology by analyzing the parameters that assurequality in agile software.

Related papers

• Lingma SWE-GPT: An Open Development-Process-Centric Language Model for Automated Software Improvement [62.94719119451089]
  Lingma SWE-GPT series learns from and simulating real-world code submission activities. Lingma SWE-GPT 72B resolves 30.20% of GitHub issues, marking a significant improvement in automatic issue resolution.
  arXiv  Detail & Related papers  (2024-11-01T14:27:16Z)
• Leveraging LLMs for the Quality Assurance of Software Requirements [40.55044936397561]
  We introduce and assess the capabilities of a Large Language Model (LLM) to evaluate the quality characteristics of software requirements according to the ISO 29148 standard. We show how an LLM can assess requirements, explain its decision-making process, and examine its capacity to propose improved versions of requirements.
  arXiv  Detail & Related papers  (2024-08-20T14:17:50Z)
• How to Measure Performance in Agile Software Development? A Mixed-Method Study [2.477589198476322]
  The study aims to identify challenges that arise when using agile software development performance metrics in practice. Results show that while widely used performance metrics are widely used in practice, agile software development teams face challenges due to a lack of transparency and standardization as well as insufficient accuracy.
  arXiv  Detail & Related papers  (2024-07-08T19:53:01Z)
• Agent-Driven Automatic Software Improvement [55.2480439325792]
  This research proposal aims to explore innovative solutions by focusing on the deployment of agents powered by Large Language Models (LLMs) The iterative nature of agents, which allows for continuous learning and adaptation, can help surpass common challenges in code generation. We aim to use the iterative feedback in these systems to further fine-tune the LLMs underlying the agents, becoming better aligned to the task of automated software improvement.
  arXiv  Detail & Related papers  (2024-06-24T15:45:22Z)
• State-Of-The-Practice in Quality Assurance in Java-Based Open Source Software Development [3.4800665691198565]
  We investigate whether and how quality assurance approaches are being used in conjunction in the development of 1,454 popular open source software projects on GitHub. Our study indicates that typically projects do not follow all quality assurance practices together with high intensity. In general, our study provides a deeper understanding of how existing quality assurance approaches are currently being used in Java-based open source software development.
  arXiv  Detail & Related papers  (2023-06-16T07:43:11Z)
• Quantifying Process Quality: The Role of Effective Organizational Learning in Software Evolution [0.0]
  Real-world software applications must constantly evolve to remain relevant. Traditional methods of software quality control involve software quality models and continuous code inspection tools. However, there is a strong correlation and causation between the quality of the development process and the resulting software product.
  arXiv  Detail & Related papers  (2023-05-29T12:57:14Z)
• Genetic Micro-Programs for Automated Software Testing with Large Path Coverage [0.0]
  Existing software testing techniques focus on utilising search algorithms to discover input values that achieve high execution path coverage. This paper outlines a novel genetic programming framework, where the evolved solutions are not input values, but micro-programs that can repeatedly generate input values. We argue that our approach can be generalised such as to be applied to many different software systems, and is thus not specific to merely the particular software component on which it was trained.
  arXiv  Detail & Related papers  (2023-02-14T18:47:21Z)
• Empowered and Embedded: Ethics and Agile Processes [60.63670249088117]
  We argue that ethical considerations need to be embedded into the (agile) software development process. We put emphasis on the possibility to implement ethical deliberations in already existing and well established agile software development processes.
  arXiv  Detail & Related papers  (2021-07-15T11:14:03Z)
• Technology Readiness Levels for Machine Learning Systems [107.56979560568232]
  Development and deployment of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. We have developed a proven systems engineering approach for machine learning development and deployment. Our "Machine Learning Technology Readiness Levels" framework defines a principled process to ensure robust, reliable, and responsible systems.
  arXiv  Detail & Related papers  (2021-01-11T15:54:48Z)
• Technology Readiness Levels for AI & ML [79.22051549519989]
  Development of machine learning systems can be executed easily with modern tools, but the process is typically rushed and means-to-an-end. Engineering systems follow well-defined processes and testing standards to streamline development for high-quality, reliable results. We propose a proven systems engineering approach for machine learning development and deployment.
  arXiv  Detail & Related papers  (2020-06-21T17:14:34Z)
• Towards CRISP-ML(Q): A Machine Learning Process Model with Quality Assurance Methodology [53.063411515511056]
  We propose a process model for the development of machine learning applications. The first phase combines business and data understanding as data availability oftentimes affects the feasibility of the project. The sixth phase covers state-of-the-art approaches for monitoring and maintenance of a machine learning applications.
  arXiv  Detail & Related papers  (2020-03-11T08:25:49Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.