Cost-Effective Supervised Learning Models for Software Effort Estimation in Agile Environments

Moharreri, Kayhan; Sapre, Alhad Vinayak; Ramanathan, Jay; Ramnath, Rajiv

doi:10.1109/compsac.2016.85

Cited by 30 publications

(15 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…That hybrid method can improve the effectiveness of effort estimates. Moharreri et al (2016) complemented manual PP with an automated estimation through extracted Agile story cards and their actual effort time. The Auto-Estimate was developed by comparing alternate methods like Naive Bayes, Random Forest, J48, and Logistic Model Tree (LMT), whereas the better result was from the combination of J48+PP, J48, and LMT+PP.…”

Section: Conducting Phase Resultsmentioning

confidence: 99%

A Review Article on Software Effort Estimation in Agile Methodology

Sudarmaningtyas¹,

Mohamed²

2021

JST

View full text Add to dashboard Cite

Currently, Agile software development method has been commonly used in software development projects, and the success rate is higher than waterfall projects. The effort estimation in Agile is still a challenge because most existing means are developed based on the conventional method. Therefore, this study aimed to ascertain the software effort estimation method that is applied in Agile, the implementation approach, and the attributes that affect effort estimation. The results showed the top three estimation that is applied in Agile, are machine learning (37%), Expert Judgement (26%), and Algorithmic (21%). The implementation of all machine learning methods used a hybrid approach, which is a combination of machine learning and expert judgement, or a mix of two or more machine learning. Meanwhile, the implementation of effort estimation through a hybrid approach was only used in 47% of relevant articles. In addition, effort estimation in Agile involved twenty-four attributes, where Complexity, Experience, Size, and Time are the most commonly used and implemented.

show abstract

Section: Conducting Phase Resultsmentioning

confidence: 99%

A Review Article on Software Effort Estimation in Agile Methodology

Sudarmaningtyas¹,

Mohamed²

2021

JST

View full text Add to dashboard Cite

show abstract

“…Another research area within Agile software development where ML models are often used is in effort estimation. Software development effort estimation is the process of estimating the effort required by the software development team within the Agile environment to develop and maintain software [12]. The studies by [12], and [13] used ML algorithms for effort prediction.…”

Section: Fusing Machine Learning With Agile Methodologiesmentioning

confidence: 99%

Machine Learning models to predict Agile Methodology adoption

Hanslo¹,

Tanner²

2020

Annals of Computer Science and Information Systems

View full text Add to dashboard Cite

Agile software development methodologies are used in many industries of the global economy. The Scrum framework is the predominant Agile methodology used to develop, deliver, and maintain complex software products. While the success of software projects has significantly improved while using Agile methodologies in comparison to the Waterfall methodology, a large proportion of projects continue to be challenged or fails. The primary objective of this paper is to use machine learning to develop predictive models for Scrum adoption, identifying a preliminary model with the highest prediction accuracy. The machine learning models were implemented using multiple linear regression statistical techniques. In particular, a full feature set adoption model, a transformed logarithmic adoption model, and a transformed logarithmic with omitted features adoption model were evaluated for prediction accuracy. Future research could improve upon these findings by incorporating additional model evaluation and validation techniques.

show abstract

“…The automated estimation method Agile efficiently applies the automatic card estimation method to historical predicted human data with the latest ML algorithms [31]. The "Auto Estimate" approach boosts the most popular form of manual poker preparation in agile environments [154].…”

Section: Recommendations For Management Software Processmentioning

confidence: 99%

Software Project Management Using Machine Learning Technique—A Review

et al. 2021

View full text Add to dashboard Cite

Project management planning and assessment are of great significance in project performance activities. Without a realistic and logical plan, it isn’t easy to handle project management efficiently. This paper presents a wide-ranging comprehensive review of papers on the application of Machine Learning in software project management. Besides, this paper presents an extensive literature analysis of (1) machine learning, (2) software project management, and (3) techniques from three main libraries, Web Science, Science Directs, and IEEE Explore. One-hundred and eleven papers are divided into four categories in these three repositories. The first category contains research and survey papers on software project management. The second category includes papers that are based on machine-learning methods and strategies utilized on projects; the third category encompasses studies on the phases and tests that are the parameters used in machine-learning management and the final classes of the results from the study, contribution of studies in the production, and the promotion of machine-learning project prediction. Our contribution also offers a more comprehensive perspective and a context that would be important for potential work in project risk management. In conclusion, we have shown that project risk assessment by machine learning is more successful in minimizing the loss of the project, thereby increasing the likelihood of the project success, providing an alternative way to efficiently reduce the project failure probabilities, and increasing the output ratio for growth, and it also facilitates analysis on software fault prediction based on accuracy.

show abstract

Cost-Effective Supervised Learning Models for Software Effort Estimation in Agile Environments

Cited by 30 publications

References 14 publications

A Review Article on Software Effort Estimation in Agile Methodology

A Review Article on Software Effort Estimation in Agile Methodology

Machine Learning models to predict Agile Methodology adoption

Software Project Management Using Machine Learning Technique—A Review

Contact Info

Product

Resources

About