# Do Not Take It for Granted: Comparing Open-Source Libraries for Software Development Effort Estimation

arXiv: https://arxiv.org/abs/2207.01705 · HTML: https://mlsanity.com/paper/2207.01705/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 77 references that are in the map.
- First posted on arXiv 4 Jul 2022.

## Builds on (most cited first, 8 of 8)

- [Machine Learning Testing: Survey, Landscapes and Horizons](https://mlsanity.com/paper/1906.10742/) (2019)
- [The Impact of Automated Parameter Optimization on Defect Prediction Models](https://mlsanity.com/paper/1801.10270/) (2018)
- [A Benchmark Study on Sentiment Analysis for Software Engineering Research](https://mlsanity.com/paper/1803.06525/) (2018)
- [What Do Developers Ask About ML Libraries? A Large-scale Study Using Stack Overflow](https://mlsanity.com/paper/1906.11940/) (2019)
- [Documentation of Machine Learning Software](https://mlsanity.com/paper/2001.11956/) (2020)
- [Evaluating prediction systems in software project estimation](https://mlsanity.com/paper/2101.05426/) (2021)
- [Run, Forest, Run? On Randomization and Reproducibility in Predictive Software Engineering](https://mlsanity.com/paper/2012.08387/) (2020)
- [Requirements of API Documentation: A Case Study into Computer Vision Services](https://mlsanity.com/paper/2012.13728/) (2020)

## Abstract (arXiv)

In the past two decades, several Machine Learning (ML) libraries have become freely available. Many studies have used such libraries to carry out empirical investigations on predictive Software Engineering (SE) tasks. However, the differences stemming from using one library over another have been overlooked, implicitly assuming that using any of these libraries would provide the user with the same or very similar results. This paper aims at raising awareness of the differences incurred when using different ML libraries for software development effort estimation (SEE), one of most widely studied SE prediction tasks. To this end, we investigate 4 deterministic machine learners as provided by 3 of the most popular ML open-source libraries written in different languages (namely, Scikit-Learn, Caret and Weka). We carry out a thorough empirical study comparing the performance of the machine learners on 5 SEE datasets in the two most common SEE scenarios (i.e., out-of-the-box-ml and tuned-ml) as well as an in-depth analysis of the documentation and code of their APIs. The results of our study reveal that the predictions provided by the 3 libraries differ in 95% of the cases on average across a total of 105 cases studied. These differences are significantly large in most cases and yield misestimations of up to approx. 3,000 hours per project. Moreover, our API analysis reveals that these libraries provide the user with different levels of control on the parameters one can manipulate, and a lack of clarity and consistency, overall, which might mislead users. Our findings highlight that the ML library is an important design choice for SEE studies, which can lead to a difference in performance. However, such a difference is under-documented. We conclude by highlighting open-challenges with suggestions for the developers of libraries as well as for the researchers and practitioners using them.