Estimation of project success using Bayesian classifier

Abstract

The software projects are considered to be successful if the cost and the duration are within the estimated ones and the quality is satisfactory. To attain project success, the project management, in which the final status of project is estimated, must be incorporated.In this paper, we consider estimation of the final status(that is, successful or unsuccessful) of project by applying Bayesian classifier to metrics data collected from project. In order to attain high estimation accuracy rate, we must select only a set of appropriate metrics to be applied. Here we consider two selection methods: the first method by the experts and the second method by the statistical test.Then we conducted an experiment using 28 project data and 29 metrics data in an organization of a certain company. The result showed that the method by the test gave higher accuracy rates than the method by the experts, and Bayesian classifier with the test method is effective to estimate project success.

Artificial Intelligence Applied to Project Success: A Literature Review

“…Furthermore we find papers that try to forecast project success for the duration of the project life cycle in its early stage, or at any other time point of the project [38], [40], [42], [51]- [54]the project management, in which the final status of project is estimated, must be incorporated.In this paper, we consider estimation of the final status(that is, successful or unsuccessfulDuring the literature review, these algorithms applied to project success prediction have been found: Artificial Intelligence application for project success predicting is relatively recent, since the first reference is from 2006. This model estimates project final state applying a Bayesian classifier to different metrics collected from a project.…”

“…The study is supported by data collected from 28 software development in-house projects. Results show that an accurate success prediction can be made, but having the right metrics is a key issue for getting accurate results [51].…”

Learning better IV&V practices

“…• Madachy's heuristic software risk model that alerts when certain observations are seen in a project [20]; • Boehm and Basili's top-10 defect reduction list [6,21] [29,30].…”

“…It is hard to make critical audit conclusions based on a Delphi-style analysis or inaccessible data. In general, only a minority of SE researchers can publish the data used to make their conclusions (for example, [22][23][24]29,30] Also, several of the these papers [27,28] only offer general advice about how to avoid problems in software development. The subjective nature of this advice makes it difficult to consistently deploy them over a national software development program.…”

“…The subjective nature of this advice makes it difficult to consistently deploy them over a national software development program. Only a few [23,24,29,30] offer publicdomain versions of their data and models.…”