Agile Software Development Framework in a Small Project Environment

Lee, Seiyoung; Yong, Hwan Seung

doi:10.3745/jips.2013.9.1.069

Cited by 29 publications

(20 citation statements)

References 21 publications

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“…There also are guidelines to harmonize quality frameworks (Baldassarre et al 2012). Finally, the approach presented in (Taylor et al 2006) and the Agile Framework for Small Projects (AFSP) (Lee and Yong 2013) are derived from Boehm and Turner's Agility/Discipline assessment.…”

Section: Software Process Improvement In Small Organizationsmentioning

confidence: 99%

Understanding the gap between software process practices and actual practice in very small companies

Sánchez‐Gordón

O’Connor

2015

Software Qual J

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This paper reports on a grounded theory to study into software developers' use of software development process in actual practice in the specific context of very small companies. This study was conducted in three very small software product companies located in Ecuador. The data collection was based on semi-structured qualitative interviews with software project managers, focus group with software developers and was supplemented by literature and documents studies. We interviewed two types of participants (managers and developers), so as to ensure that we elicited a holistic perspective of how they approached the software development process in actual practice. The goal was to study what practices are actually used and their opinion and attitude towards the potential adopting of an international standard (ISO/IEC 29110) specifically designed for very small companies. With the collected data we performed an analysis utilizing Grounded Theory coding techniques, as this methodology promotes the focus on uncovering the real concerns of the participants. This study highlighted three areas of concern: customer, software product and development tasks coordination and tracking. The findings in this study give an insight towards the work products as they relate to software development process practices in very small companies and the important factors that must be considered to assist project success.

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Section: Software Process Improvement In Small Organizationsmentioning

confidence: 99%

Understanding the gap between software process practices and actual practice in very small companies

Sánchez‐Gordón

O’Connor

2015

Software Qual J

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“…Lee and et al, introduced a framework to evaluate agile software development in small projects based on success factors. This paper aims to obtain a suitable success factors to help small agile software projects to succeed [13]. This paper is based on success factors that have impact small software projects by statistical methods.…”

Section: Related Workmentioning

confidence: 99%

Multiple Linear Regression for Determining Critical Failure Factors of Agile Software Projects

Abdelaziz¹,

Darwish²,

Hefny³

2019

IJIES

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The management of agile software projects plays an important role in the success or failure of projects. There are many factors of success or failure that clearly affect agile software projects. There is a big problem in not having a formal study to identify critical failure factors that assess the success or failure of projects accurately. This paper focuses on failure factors of agile software projects. This paper seeks to configure a formal study by using multiple regression analysis for identifying critical failure factors to avoid projects failure, thus saving time and money for the stakeholders. The proposed model is implemented by using R programming. The proposed model also is implemented by two models. The first model based on critical failure factors (CFF) of agile software projects. The second model based the failure factors of the proposed in the initial list (FFIL). The model of CFF outperforms on the model of FFIL in order to accuracy by 10.7% and random error ratio by 4.8%. The results revealed that only 10 out of 19 hypotheses were supported, identifying seven critical failure factors for Agile software development projects: (a) Organizational culture too traditional, (b) Lack of agile logistical arrangements,(c) Lack of project management competence, (d) Lack of team work, (e) Bad customer relationship, (f) defined project scope, and (g) defined project planning.

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“…Lee and et al, presented a new framework to evaluate agile methodology in small projects based on agility factors are composed of flexibility, speed, leanness, learning and responsiveness. This paper implemented experiments on small projects and using scrum method [14]. Researcher opinion, agility factors may find the best evaluate small projects in agile methodology.…”

Section: Statistical Techniques Of Agile Software Projectsmentioning

confidence: 99%

Towards a Machine Learning Model for Predicting Failure of Agile Software Projects

Mohamed¹,

Darwish²,

Hefny³

2017

IJCA

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Agile software development plays a very significant role in software projects. Agile software project is a refined approach to design and direct project processes. An agile project is finished in short sections called iterations. This paper introduces a survey of machine learning approaches for predicting failure of agile software projects. It reviews the uses of machine learning techniques such as fuzzy logic, multiple linear regression, neural network, logistic regression and etc., for predicting success and failure of agile software projects. This paper also proposes machine learning model for predicting failure of agile software projects. Many researches in this topic were reviewed, analyzed, summarized, and compared according to the used machine learning techniques in agile software projects.

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Agile Software Development Framework in a Small Project Environment

Cited by 29 publications

References 21 publications

Understanding the gap between software process practices and actual practice in very small companies

Understanding the gap between software process practices and actual practice in very small companies

Multiple Linear Regression for Determining Critical Failure Factors of Agile Software Projects

Towards a Machine Learning Model for Predicting Failure of Agile Software Projects

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