Linear programming as a baseline for software effort estimation

Federica Sarro*, Alessio Petrozziello

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

76 Downloads (Pure)

Abstract

Software effort estimation studies still suffer from discordant empirical results (i.e., conclusion instability) mainly due to the lack of rigorous benchmarking methods. So far only one baseline model, namely, Automatically Transformed Linear Model (ATLM), has been proposed yet it has not been extensively assessed. In this article, we propose a novel method based on Linear Programming (dubbed as Linear Programming for Effort Estimation, LP4EE) and carry out a thorough empirical study to evaluate the effectiveness of both LP4EE and ATLM for benchmarking widely used effort estimation techniques. The results of our study confirm the need to benchmark every other proposal against accurate and robust baselines. They also reveal that LP4EE is more accurate than ATLM for 17% of the experiments and more robust than ATLM against different data splits and cross-validation methods for 44% of the cases. These results suggest that using LP4EE as a baseline can help reduce conclusion instability. We make publicly available an open-source implementation of LP4EE in order to facilitate its adoption in future studies.

Original languageEnglish
Article number12
JournalACM Transactions on Software Engineering and Methodology
Volume27
Issue number3
DOIs
Publication statusPublished - 1 Sep 2018

Keywords

  • Benchmarking
  • Linear programming
  • Software effort estimation

Fingerprint

Dive into the research topics of 'Linear programming as a baseline for software effort estimation'. Together they form a unique fingerprint.

Cite this