Software engineering practices for machine learning — Adoption, effects, and team assessment
Notice bibliographique
Résumé
Machine learning (ML) is extensively used in production-ready applications, calling for mature engineering techniques to ensure robust development, deployment and maintenance. Given the potential negative impact machine learning (ML) can have on people, society or the environment, engineering techniques that can ensure robustness against technical errors and adversarial attacks are of considerable importance. In this work, we investigate how teams of experts develop, deploy and maintain software with ML components. Moreover, we link what teams do to the effects they aim to achieve and provide means for improvement. Towards this goal, we performed a mixed-methods study with a sequential exploratory strategy. First, we performed a systematic literature review through which we mined both academic and grey literature, and compiled a catalogue of engineering practices for ML. Second, we validated this catalogue using a large-scale survey, which measured the degree of adoption of the practices and their perceived effects. Third, we ran validation interviews with practitioners to add depth to the survey results. The catalogue covers a broad range of practices for engineering software systems with ML components and for ensuring non-functional properties that fall under the umbrella of trustworthy ML, such as fairness, security or accountability. Here, we present the results of our study, which indicate, for example, that larger and more experienced teams tend to adopt more practices, but that trustworthiness practices tend to be neglected. Moreover, we show that the effects measured in our survey, such as team agility or accountability, can be predicted quite accurately from groups of practices. This allowed us to contrast the importance of the practices for these effects as well as adoption rates, revealing, for example, that widely adopted practices are, in reality, less important with respect to some effects. For instance, writing reusable scripts for data cleaning and merging is highly adopted, but has a limited impact on reproducibility. Overall, our study provides a quantitative assessment of ML engineering practices and their impact on desirable properties of software with ML components, by which we open multiple avenues for improving the adoption of useful practices. Editor’s note: Open Science material was validated by the Journal of Systems and Software Open Science Board.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,006 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,001 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».