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Multi-objective software effort estimation

Authors:

Federica Sarro,

Alessio Petrozziello,

Mark HarmanAuthors Info & Claims

ICSE '16: Proceedings of the 38th International Conference on Software Engineering

Pages 619 - 630

https://doi.org/10.1145/2884781.2884830

Published: 14 May 2016 Publication History

Abstract

We introduce a bi-objective effort estimation algorithm that combines Confidence Interval Analysis and assessment of Mean Absolute Error. We evaluate our proposed algorithm on three different alternative formulations, baseline comparators and current state-of-the-art effort estimators applied to five real-world datasets from the PROMISE repository, involving 724 different software projects in total. The results reveal that our algorithm outperforms the baseline, state-of-the-art and all three alternative formulations, statistically significantly (p < 0.001) and with large effect size (Â₁₂ ≥ 0.9) over all five datasets. We also provide evidence that our algorithm creates a new state-of-the-art, which lies within currently claimed industrial human-expert-based thresholds, thereby demonstrating that our findings have actionable conclusions for practicing software engineers.

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Index Terms

Multi-objective software effort estimation
1. Social and professional topics
  1. Professional topics
    1. Management of computing and information systems
      1. Project and people management
2. Software and its engineering
  1. Software creation and management
    1. Software development process management
  2. Software notations and tools
    1. Software configuration management and version control systems

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Published In

cover image ACM Conferences

ICSE '16: Proceedings of the 38th International Conference on Software Engineering

May 2016

1235 pages

ISBN:9781450339001

DOI:10.1145/2884781

General Chair:
Laura Dillon
Michigan State University
,
Program Chairs:
Willem Visser
Stellenbosch University, South Africa
,
Laurie Williams
North Carolina State University

Copyright © 2016 Owner/Author.

This work is licensed under a Creative Commons Attribution International 4.0 License.

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ACM: Association for Computing Machinery
SIGSOFT: ACM Special Interest Group on Software Engineering
IEEE-CS\TCSE: TC on Software Engineering
IEEE-CS\DATC: IEEE Computer Society

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Association for Computing Machinery

New York, NY, United States

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Published: 14 May 2016

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ICSE '16: 38th International Conference on Software Engineering

May 14 - 22, 2016

Texas, Austin

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https://dl.acm.org/doi/10.1145/3691620.3695540
Lustosa AMenzies T(2024)Learning from Very Little Data: On the Value of Landscape Analysis for Predicting Software Project HealthACM Transactions on Software Engineering and Methodology10.1145/363025233:3(1-22)Online publication date: 14-Mar-2024
https://dl.acm.org/doi/10.1145/3630252
Li YRen ZWang ZYang LDong LZhong CZhang HRoychoudhury APaiva AAbreu RStorey M(2024)Fine-SE: Integrating Semantic Features and Expert Features for Software Effort EstimationProceedings of the IEEE/ACM 46th International Conference on Software Engineering10.1145/3597503.3623349(1-12)Online publication date: 20-May-2024
https://dl.acm.org/doi/10.1145/3597503.3623349
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