Dependabot and security pull requests: large empirical study
Notice bibliographique
Résumé
Modern software development is a complex engineering process where developer code cohabits with an increasingly larger number of external open-source components. Even though these components facilitate sharing and reusing code along with other benefits related to maintenance and code quality, they are often the seeds of vulnerabilities in the software supply chain leading to attacks with severe consequences. Indeed, one common strategy used to conduct attacks is to exploit or inject other security flaws in new versions of dependency packages. It is thus important to keep dependencies updated in a software development project. Unfortunately, several prior studies have highlighted that, to a large extent, developers struggle to keep track of the dependency package updates, and do not quickly incorporate security patches. Therefore, automated dependency-update bots have been proposed to mitigate the impact and the emergence of vulnerabilities in open-source projects. In our study, we focus on Dependabot, a dependency management bot that has gained popularity on GitHub recently. It allows developers to keep a lookout on project dependencies and reduce the effort of monitoring the safety of the software supply chain. We performed a large empirical study on dependency updates and security pull requests to understand: (1) the degree and reasons of Dependabot’s popularity; (2) the patterns of developers’ practices and techniques to deal with vulnerabilities in dependencies; (3) the management of security pull requests (PRs), the threat lifetime, and the fix delay; and (4) the factors that significantly correlate with the acceptance of security PRs and fast merges. To that end, we collected a dataset of 9,916,318 pull request-related issues made in 1,743,035 projects on GitHub for more than 10 different programming languages. In addition to the comprehensive quantitative analysis, we performed a manual qualitative analysis on a representative sample of the dataset, and we substantiated our findings by sending a survey to developers that use dependency management tools. Our study shows that Dependabot dominates more than 65% of dependency management activity, mainly due to its efficiency, accessibility, adaptivity, and availability of support. We also found that developers handle dependency vulnerabilities differently, but mainly rely on the automation of PRs generation to upgrade vulnerable dependencies. Interestingly, Dependabot’s and developers’ security PRs are highly accepted, and the automation allows to accelerate their management, so that fixes are applied in less than one day. However, the threat of dependency vulnerabilities remains hidden for 512 days on average, and patches are disclosed after 362 days due to the reliance on the manual effort of security experts. Also, project characteristics, the amount of PR changes, as well as developer and dependency features seem to be highly correlated with the acceptance and fast merges of security PRs.
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 machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,017 | 0,093 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,003 | 0,005 |
| Études des sciences et des technologies | 0,002 | 0,002 |
| Communication savante | 0,002 | 0,005 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,002 | 0,006 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,005 | 0,002 |
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 source (Gemma direct ou Codex distillé), 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 ».