An Effort Estimation Support Tool for Agile Software Development: An Empirical Evaluation

Abstract

Accurate effort estimation is an important part of the software process. In Agile Software Development, the techniques for predicting effort are mostly based on expert judgment, but there are approaches based on Machine Learning. The theme continues to be challenging and a subject of further studies given the difficulty of finding accurate solutions to the problem. This paper proposes and evaluates a tool based on the decision tree method for effort estimation in agile projects. We evaluated our tool given its accuracy and ease of use collecting data from four projects. To evaluate the accuracy, we compared the values of Magnitude of Relative Error from the teams' estimations with the values provided by the tool. To evaluate the ease of use, we used the Technology Acceptance Mode. The initial results show that the tool can be reliably used with minimal training. In terms of accuracy, the tool achieved lower error compared to the estimates provided by the teams (mean: 19.05% vs 33.32%), and the evaluation means in TAM were higher than 4.0 in ten of the eleven variables analyzed on a Likert scale. From this work, we conclude that estimation by decision tree is a viable technique that, at the very least, can be used by project managers to complement current estimation techniques.

A Genetic Algorithm-Based Approach to Support Forming Multiple Scrum Project Teams

“…In agile projects, including Scrum, the most popular notation to document business requirements is the User Story [52]. Using an approach such as the ones presented in Dantas et al [5], Ramos et al [6], and Elamin and Osman [15] to add link semantics between US enable inferring the similarity between them; thus, facilitating the reuse of US for new projects. Since most agile tools automatically construct traceability between US and tasks (i.e., a one-to- many relationship), it is possible to reuse tasks having the US as the index.…”

“…For organizations with a high rate of similarity between projects, there is the potential for reusing knowledge from past projects to assist in decision making. For instance, Dantas et al [5] analyzed the similarity between the features of a target project and historical data to estimate effort, and Ramos et al [6] have done the same to infer non-functional requirements. A similar approach is possible to address the MTF problem if the company has detailed information about the work done by each collaborator.…”

“…This information is used to build a detailed profile of the developers. Plus, with the support of a US-reuse framework, such as the ones presented in Dantas et al [5], Ramos et al [6], and Elamin and Osman [15], it enables to predict the tasks to be developed in projects before their execution, enabling to build detailed project profiles. Second, the detailed developers' and projects' profiles are used as input into a Genetic Algorithm, which searches for the best global team formation for Scrum projects.…”