Mouna Abidi

Pierre-Olivier Côté, Amin Nikanjam, Rached Bouchoucha et al.

Context: An increasing demand is observed in various domains to employ Machine Learning (ML) for solving complex problems. ML models are implemented as software components and deployed in Machine Learning Software Systems (MLSSs). Problem: There is a strong need for ensuring the serving quality of MLSSs. False or poor decisions of such systems can lead to malfunction of other systems, significant financial losses, or even threats to human life. The quality assurance of MLSSs is considered a challenging task and currently is a hot research topic. Objective: This paper aims to investigate the characteristics of real quality issues in MLSSs from the viewpoint of practitioners. This empirical study aims to identify a catalog of quality issues in MLSSs. Method: We conduct a set of interviews with practitioners/experts, to gather insights about their experience and practices when dealing with quality issues. We validate the identified quality issues via a survey with ML practitioners. Results: Based on the content of 37 interviews, we identified 18 recurring quality issues and 21 strategies to mitigate them. For each identified issue, we describe the causes and consequences according to the practitioners' experience. Conclusion: We believe the catalog of issues developed in this study will allow the community to develop efficient quality assurance tools for ML models and MLSSs. A replication package of our study is available on our public GitHub repository

Moses Openja, Foutse Khomh, Armstrong Foundjem et al.

Recently, machine and deep learning (ML/DL) algorithms have been increasingly adopted in many software systems. Due to their inductive nature, ensuring the quality of these systems remains a significant challenge for the research community. Unlike traditional software built deductively by writing explicit rules, ML/DL systems infer rules from training data. Recent research in ML/DL quality assurance has adapted concepts from traditional software testing, such as mutation testing, to improve reliability. However, it is unclear if these proposed testing techniques are adopted in practice, or if new testing strategies have emerged from real-world ML deployments. There is little empirical evidence about the testing strategies. To fill this gap, we perform the first fine-grained empirical study on ML testing in the wild to identify the ML properties being tested, the testing strategies, and their implementation throughout the ML workflow. We conducted a mixed-methods study to understand ML software testing practices. We analyzed test files and cases from 11 open-source ML/DL projects on GitHub. Using open coding, we manually examined the testing strategies, tested ML properties, and implemented testing methods to understand their practical application in building and releasing ML/DL software systems. Our findings reveal several key insights: 1.) The most common testing strategies, accounting for less than 40%, are Grey-box and White-box methods, such as Negative Testing, Oracle Approximation and Statistical Testing. 2.) A wide range of 17 ML properties are tested, out of which only 20% to 30% are frequently tested, including Consistency, Correctness}, and Efficiency. 3.) Bias and Fairness is more tested in Recommendation, while Security & Privacy is tested in Computer Vision (CV) systems, Application Platforms, and Natural Language Processing (NLP) systems.

Mouna Abidi, Md Saidur Rahman, Moses Openja et al.

Nowadays, modern applications are developed using components written in different programming languages. These systems introduce several advantages. However, as the number of languages increases, so does the challenges related to the development and maintenance of these systems. In such situations, developers may introduce design smells (i.e., anti-patterns and code smells) which are symptoms of poor design and implementation choices. Design smells are defined as poor design and coding choices that can negatively impact the quality of a software program despite satisfying functional requirements. Studies on mono-language systems suggest that the presence of design smells affects code comprehension, thus making systems harder to maintain. However, these studies target only mono-language systems and do not consider the interaction between different programming languages. In this paper, we present an approach to detect multi-language design smells in the context of JNI systems. We then investigate the prevalence of those design smells. Specifically, we detect 15 design smells in 98 releases of nine open-source JNI projects. Our results show that the design smells are prevalent in the selected projects and persist throughout the releases of the systems. We observe that in the analyzed systems, 33.95% of the files involving communications between Java and C/C++ contains occurrences of multi-language design smells. Some kinds of smells are more prevalent than others, e.g., Unused Parameters, Too Much Scattering, Unused Method Declaration. Our results suggest that files with multi-language design smells can often be more associated with bugs than files without these smells, and that specific smells are more correlated to fault-proneness than others.