Recommending Analogical APIs via Knowledge Graph Embedding

Library migration, which re-implements the same software behavior by using a different library instead of using the current one, has been widely observed in software evolution. One essential part of library migration is to find an analogical API that could provide the same functionality as current ones. However, given the large number of libraries/APIs, manually finding an analogical API could be very time-consuming and error-prone. Researchers have developed multiple automated analogical API recommendation techniques. Documentation-based methods have particularly attracted significant interest. Despite their potential, these methods have limitations, such as a lack of comprehensive semantic understanding in documentation and scalability challenges. In this work, we propose KGE4AR, a novel documentation-based approach that leverages knowledge graph (KG) embedding to recommend analogical APIs during library migration. Specifically, KGE4AR proposes a novel unified API KG to comprehensively and structurally represent three types of knowledge in documentation, which can better capture the high-level semantics. Moreover, KGE4AR then proposes to embed the unified API KG into vectors, enabling more effective and scalable similarity calculation. We build KGE4AR' s unified API KG for 35,773 Java libraries and assess it in two API recommendation scenarios: with and without target libraries. Our results show that KGE4AR substantially outperforms state-of-the-art documentation-based techniques in both evaluation scenarios in terms of all metrics (e.g., 47.1%-143.0% and 11.7%-80.6% MRR improvements in each scenario). Additionally, we explore KGE4AR' s scalability, confirming its effective scaling with the growing number of libraries.Comment: Accepted by FSE 202

Fuzzing Deep-Learning Libraries via Automated Relational API Inference

A growing body of research has been dedicated to DL model testing. However, there is still limited work on testing DL libraries, which serve as the foundations for building, training, and running DL models. Prior work on fuzzing DL libraries can only generate tests for APIs which have been invoked by documentation examples, developer tests, or DL models, leaving a large number of APIs untested. In this paper, we propose DeepREL, the first approach to automatically inferring relational APIs for more effective DL library fuzzing. Our basic hypothesis is that for a DL library under test, there may exist a number of APIs sharing similar input parameters and outputs; in this way, we can easily "borrow" test inputs from invoked APIs to test other relational APIs. Furthermore, we formalize the notion of value equivalence and status equivalence for relational APIs to serve as the oracle for effective bug finding. We have implemented DeepREL as a fully automated end-to-end relational API inference and fuzzing technique for DL libraries, which 1) automatically infers potential API relations based on API syntactic or semantic information, 2) synthesizes concrete test programs for invoking relational APIs, 3) validates the inferred relational APIs via representative test inputs, and finally 4) performs fuzzing on the verified relational APIs to find potential inconsistencies. Our evaluation on two of the most popular DL libraries, PyTorch and TensorFlow, demonstrates that DeepREL can cover 157% more APIs than state-of-the-art FreeFuzz. To date, DeepREL has detected 162 bugs in total, with 106 already confirmed by the developers as previously unknown bugs. Surprisingly, DeepREL has detected 13.5% of the high-priority bugs for the entire PyTorch issue-tracking system in a three-month period. Also, besides the 162 code bugs, we have also detected 14 documentation bugs (all confirmed).Comment: Accepted at ESEC/FSE 202