Many-objective evolutionary algorithms: A survey

B Li, J Li, K Tang, X Yao - ACM Computing Surveys (CSUR), 2015‏ - dl.acm.org
Multiobjective evolutionary algorithms (MOEAs) have been widely used in real-world
applications. However, most MOEAs based on Pareto-dominance handle many-objective …

Systematic literature review of ensemble effort estimation

A Idri, M Hosni, A Abran - Journal of Systems and Software, 2016‏ - Elsevier
The need to overcome the weaknesses of single estimation techniques for prediction tasks
has given rise to ensemble methods in software development effort estimation (SDEE). An …

Software defect prediction: do different classifiers find the same defects?

D Bowes, T Hall, J Petrić - Software Quality Journal, 2018‏ - Springer
During the last 10 years, hundreds of different defect prediction models have been
published. The performance of the classifiers used in these models is reported to be similar …

Multi-objective software effort estimation

F Sarro, A Petrozziello, M Harman - Proceedings of the 38th International …, 2016‏ - dl.acm.org
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 …

Heterogeneous ensemble model to optimize software effort estimation accuracy

SS Ali, J Ren, K Zhang, J Wu, C Liu - IEEE Access, 2023‏ - ieeexplore.ieee.org
The software industry has experienced rapid expansion in recent years, with software
development now essential to the success of many multinational corporations. The demand …

How to evaluate solutions in pareto-based search-based software engineering: A critical review and methodological guidance

M Li, T Chen, X Yao - IEEE Transactions on Software …, 2020‏ - ieeexplore.ieee.org
With modern requirements, there is an increasing tendency of considering multiple
objectives/criteria simultaneously in many Software Engineering (SE) scenarios. Such a …

Mitigating unfairness via evolutionary multiobjective ensemble learning

Q Zhang, J Liu, Z Zhang, J Wen… - IEEE transactions on …, 2022‏ - ieeexplore.ieee.org
In the literature of mitigating unfairness in machine learning (ML), many fairness measures
are designed to evaluate predictions of learning models and also utilized to guide the …

Multi-objective feature attribution explanation for explainable machine learning

Z Wang, C Huang, Y Li, X Yao - ACM Transactions on Evolutionary …, 2024‏ - dl.acm.org
The feature attribution-based explanation (FAE) methods, which indicate how much each
input feature contributes to the model's output for a given data point, are one of the most …

Research patterns and trends in software effort estimation

SK Sehra, YS Brar, N Kaur, SS Sehra - Information and Software …, 2017‏ - Elsevier
Context Software effort estimation (SEE) is most crucial activity in the field of software
engineering. Vast research has been conducted in SEE resulting into a tremendous …

Search-based software library recommendation using multi-objective optimization

A Ouni, RG Kula, M Kessentini, T Ishio… - Information and …, 2017‏ - Elsevier
Context: Software library reuse has significantly increased the productivity of software
developers, reduced time-to-market and improved software quality and reusability. However …