Task purpose estimation in software development based on automatic measurement data and machine learning

Ryota Ohashi, Kenichi Matsumoto, Hidetake Uwano, Akito Monden, Kenji Araki, Kingo Yamada

Research output: Contribution to journalArticle

2 Citations (Scopus)

Abstract

In this paper we propose a method to support Personal Software Process (PSP), which is a well known software process improvement framework for individual developers. The proposed method estimates developer's purposes (aims) from time-series data about developer's tasks, given by an execution history of software applications. We implemented the method by a machine learning algorithm, Random Forests. The experiment result shows the prediction with the time-series data is more accurate than the prediction without the time-series data. Especially, when using longer time-series data, accuracy of estimation became 97 %. It can be expected that the proposed method can help developers' process improvement as they become aware of how much time they spent on a specific aim such as implementation and testing.

Original languageEnglish
Pages (from-to)139-150
Number of pages12
JournalComputer Software
Volume33
Issue number2
Publication statusPublished - May 1 2016

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ASJC Scopus subject areas

  • Software

Cite this

Ohashi, R., Matsumoto, K., Uwano, H., Monden, A., Araki, K., & Yamada, K. (2016). Task purpose estimation in software development based on automatic measurement data and machine learning. Computer Software, 33(2), 139-150.