arXiv subjects

Alain Abran

Publications and source records attributed to Alain Abran.

4 recordsLinked to original sources

arXiv2026

The Hidden Environmental Cost of Poor Coding Practices in TensorFlow and Keras Applications: A Study on Resource Leaks and Carbon Emissions

Efficiency and sustainability are critical considerations in the development and deployment of machine learning (ML) applications. Among the factors influencing sustainability, resource leaks in ML code can introduce hidden inefficiencies that elevate energy consumption and CO2 emissions. Despite this, empirical evidence quantifying their environmental impact remains limited. This emerging results paper presents an initial empirical investigation of two common resource-leak smells, namely Improper Model Reuse (IMR) and Unreleased Tensor References (UTR), and their impact on energy consumption and CO2 emissions in TensorFlow and Keras workloads. Controlled experiments were conducted for each smell by executing identical training tasks while comparing against a smell-free baseline. Our preliminary results show that both smells consistently increase estimated electricity usage and carbon emissions. IMR and UTR increased electricity consumption by approximately 32% and 46%, respectively, with proportional increases in CO2 emissions. Paired statistical tests indicate that these differences are systematic and statistically significant, providing initial empirical evidence that resource-leak smells may degrade ML energy efficiency and environmental sustainability. These findings suggest that resource-leak smells pose measurable risks to both software quality and sustainability, emphasizing the importance of integrating resource-lifecycle management and energy-efficiency considerations into ML development.

cs.SE↗

arXiv2025

From Code Smells to Best Practices: Tackling Resource Leaks in PyTorch, TensorFlow, and Keras

Much of the existing ML research focuses on model performance metrics, leaving limited attention to the long-term sustainability and resource efficiency of ML applications. While high performance is essential, ensuring efficient resource management is equally critical for robust deployment. This study addresses this gap by systematically identifying code smells that lead to resource leaks in ML applications. We conducted an empirical investigation of developer discussions and real-world code snippets from PyTorch, TensorFlow, and Keras. The analysis identified 30 PyTorch-related smells and 16 TensorFlow/Keras smells linked to resource leaks. These smells were categorized in two ways: (1) based on their root causes, and (2) as general ML smells with framework-specific characteristics. For each smell, we derived at least one best practice, resulting in 50 recommended coding patterns aimed at reducing resource leakage and improving efficiency. To ensure the validity of our findings, we employed a three-phase validation process involving independent analysis by three authors followed by consensus discussions. This is the first comprehensive study to examine resource-leak-inducing code smells across major ML frameworks and to present actionable best practices for mitigating them. The contributions support developers in building more efficient and sustainable ML applications and offer a structured view of the underlying causes of resource leaks.

cs.SE↗

arXiv2019

Software Development Effort Estimation Using Regression Fuzzy Models

Software effort estimation plays a critical role in project management. Erroneous results may lead to overestimating or underestimating effort, which can have catastrophic consequences on project resources. Machine-learning techniques are increasingly popular in the field. Fuzzy logic models, in particular, are widely used to deal with imprecise and inaccurate data. The main goal of this research was to design and compare three different fuzzy logic models for predicting software estimation effort: Mamdani, Sugeno with constant output and Sugeno with linear output. To assist in the design of the fuzzy logic models, we conducted regression analysis, an approach we call regression fuzzy logic. State-of-the-art and unbiased performance evaluation criteria such as standardized accuracy, effect size and mean balanced relative error were used to evaluate the models, as well as statistical tests. Models were trained and tested using industrial projects from the International Software Benchmarking Standards Group (ISBSG) dataset. Results showed that data heteroscedasticity affected model performance. Fuzzy logic models were found to be very sensitive to outliers. We concluded that when regression analysis was used to design the model, the Sugeno fuzzy inference system with linear output outperformed the other models.

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arXiv2017

A training process for improving the quality of software projects developed by a practitioner

Background: The quality of a software product depends on the quality of the software process followed in developing the product. Therefore, many higher education institutions (HEI) and software organizations have implemented software process improvement (SPI) training courses to improve the software quality. Objective: Because the duration of a course is a concern for HEI and software organizations, we investigate whether the quality of software projects will be improved by reorganizing the activities of the ten assignments of the original personal software process (PSP) course into a modified PSP having fewer assignments (i.e., seven assignments). Method: The assignments were developed by following a modified PSP with fewer assignments but including the phases, forms, standards, and logs suggested in the original PSP. The measurement of the quality of the software assignments was based on defect density. Results: When the activities in the original PSP were reordered into fewer assignments, as practitioners progress through the PSP training, the defect density improved with statistical significance. Conclusions: Our modified PSP could be applied in academy and industrial environments which are concerned in the sense of reducing the PSP training time

cs.SE↗