(Partial) Program Dependence Learning

Aashish Yadavally, Tien N. Nguyen, Wenbo Wang, Shaohua Wang

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations


Code fragments from developer forums often migrate to applications due to the code reuse practice. Owing to the incomplete nature of such programs, analyzing them to early determine the presence of potential vulnerabilities is challenging. In this work, we introduce NeuralPDA, a neural network-based program dependence analysis tool for both complete and partial programs. Our tool efficiently incorporates intra-statement and inter-statement contextual features into statement representations, thereby modeling program dependence analysis as a statement-pair dependence decoding task. In the empirical evaluation, we report that NeuralPDA predicts the CFG and PDG edges in complete Java and C/C++ code with combined F-scores of 94.29% and 92.46%, respectively. The F-score values for partial Java and C/C++ code range from 94.29%-97.17% and 92.46%-96.01%, respectively. We also test the usefulness of the PDGs predicted by NeuralPDA (i.e., PDG*) on the downstream task of method-level vulnerability detection. We discover that the performance of the vulnerability detection tool utilizing PDG* is only 1.1% less than that utilizing the PDGs generated by a program analysis tool. We also report the detection of 14 real-world vulnerable code snippets from StackOverflow by a machine learning-based vulnerability detection tool that employs the PDGs predicted by NeuralPDA for these code snippets.

Original languageEnglish (US)
Title of host publicationProceedings - 2023 IEEE/ACM 45th International Conference on Software Engineering, ICSE 2023
PublisherIEEE Computer Society
Number of pages13
ISBN (Electronic)9781665457019
StatePublished - 2023
Event45th IEEE/ACM International Conference on Software Engineering, ICSE 2023 - Melbourne, Australia
Duration: May 15 2023May 16 2023

Publication series

NameProceedings - International Conference on Software Engineering
ISSN (Print)0270-5257


Conference45th IEEE/ACM International Conference on Software Engineering, ICSE 2023

All Science Journal Classification (ASJC) codes

  • Software


  • deep learning
  • neural networks
  • neural partial program analysis
  • neural program dependence analysis


Dive into the research topics of '(Partial) Program Dependence Learning'. Together they form a unique fingerprint.

Cite this