1,720,963 research outputs found
Proceedings of the 15th Edition of Seminar on Advanced Techniques & Tools for Software Evolution
No full textUnmasking data secrets: An empirical investigation into data smells and their impact on data quality
No full textWhen code smells meet ML: on the lifecycle of ML-specific code smells in ML-enabled systems
No full textThe adoption of Machine Learning (ML)-enabled systems is growing rapidly, introducing novel challenges in maintaining quality and managing technical debt in these complex systems. Among the key quality threats are ML-specific code smells (ML-CSs), suboptimal implementation practices in ML pipelines that can compromise system performance, reliability, and maintainability. Although these smells have been defined in the literature, detailed insights into their characteristics, evolution, and mitigation strategies are still needed to help developers address these quality issues effectively. In this paper, we investigate the emergence and evolution of ML-CSs through a large-scale empirical study focusing on (i) their prevalence in real ML-enabled systems, (ii) how they are introduced and removed, and (iii) their survivability. We analyze over 400,000 commits from 337 ML-enabled projects, leveraging CodeSmile, a novel ML smell detector that we developed to enable our investigation and identify ML-specific code smells. Our results reveal that: (1) CodeSmile can detect ML-CSs with precision and recall rates of 87.4% and 78.6%, respectively; (2) ML-CSs are frequently introduced during file modifications in new feature tasks; (3) smells are typically removed during tasks related to new features, enhancements, or refactoring; and (4) the majority of ML-CSs are resolved within the first 10% of commits. Based on these findings, we provide actionable conclusions and insights to guide future research and quality assurance practices for ML-enabled systems
Technical debt in AI-enabled systems: On the prevalence, severity, impact, and management strategies for code and architecture
No full textContext: Artificial Intelligence (AI) is pervasive in several application domains and promises to be even more diffused in the next decades. Developing high-quality AI-enabled systems — software systems embedding one or multiple AI components, algorithms, and models — could introduce critical challenges for mitigating specific risks related to the systems’ quality. Such development alone is insufficient to fully address socio-technical consequences and the need for rapid adaptation to evolutionary changes. Recent work proposed the concept of AI technical debt, a potential liability concerned with developing AI-enabled systems whose impact can affect the overall systems’ quality. While the problem of AI technical debt is rapidly gaining the attention of the software engineering research community, scientific knowledge that contributes to understanding and managing the matter is still limited. Objective: In this paper, we leverage the expertise of practitioners to offer useful insights to the research community, aiming to enhance researchers’ awareness about the detection and mitigation of AI technical debt. Our ultimate goal is to empower practitioners by providing them with tools and methods. Additionally, our study sheds light on novel aspects that practitioners might not be fully acquainted with, contributing to a deeper understanding of the subject. Method: We develop a survey study featuring 53 AI practitioners, in which we collect information on the practical prevalence, severity, and impact of AI technical debt issues affecting the code and the architecture other than the strategies applied by practitioners to identify and mitigate them. Results: The key findings of the study reveal the multiple impacts that AI technical debt issues may have on the quality of AI-enabled systems (e.g., the high negative impact that Undeclared consumers has on security, whereas Jumbled Model Architecture can induce the code to be hard to maintain) and the little support practitioners have to deal with them, limited to apply manual effort for identification and refactoring. Conclusion: We conclude the article by distilling lessons learned and actionable insights for researchers
Into the ML-Universe: An improved classification and characterization of machine-learning projects
No full text
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Full text link“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Full text linkWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
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