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Investigating Next Steps in Static API-Misuse Detection

Reviewed by Maliha Sultana / 2021-11-29
Keywords: Code Smells

Developers often use APIs to access data or web applications, but misuse of APIs can lead to data loss or software crashes. Although several API misuse detectors exist, most of them suffer from low precision and recall. To address this, Sven2019 proposes MUDETECT, an API misuse detector which builds on the strengths and addresses many of the weaknesses of previous detectors. It has 2X higher recall than previous detectors and 2.5X higher precision; moreover, it can work in cross-project settings and mines patterns across projects rather than from only the target project.

MUDETECT begins by encoding API usages as API-Usage Graphs to capture properties that can separate misuses from correct usages. It then applies a detection algorithm to mine and identify violating patterns, and a ranking strategy to rank true positives in order to improve precision.

MUDETECT was evaluated by comparing it four other API misuse detection tools (JADET, GROUMINER, TIKANGA, and DMMC) and out-performed them all with recall of 20.9%, and precision of 21.9% in a typical setting. More impressively, MUDETECT's recall and precision reached 42.2% and 33.0% in cross-project settings. Just as linting source code for style violations and common problems is now routine, this work holds out hope that checking APIs will be equally routine.

Sven2019 Amann Sven, Hoan Anh Nguyen, Sarah Nadi, Tien N. Nguyen, and Mira Mezini: "Investigating Next Steps in Static API-Misuse Detection". Proc. International Conference on Mining Software Repositories (MSR), 2019, 10.1109/msr.2019.00053.

Application Programming Interfaces (APIs) often impose constraints such as call order or preconditions. API misuses, i.e., usages violating these constraints, may cause software crashes, data-loss, and vulnerabilities. Researchers developed several approaches to detect API misuses, typically still resulting in low recall and precision. In this work, we investigate ways to improve API-misuse detection. We design MUDetect, an API-misuse detector that builds on the strengths of existing detectors and tries to mitigate their weaknesses. MUDetect uses a new graph representation of API usages that captures different types of API misuses and a systematically designed ranking strategy that effectively improves precision. Evaluation shows that MUDetect identifies real-world API misuses with twice the recall of previous detectors and 2.5x higher precision. It even achieves almost 4x higher precision and recall, when mining patterns across projects, rather than from only the target project.