mlsanityData to 14 Oct 2026

STraceBERT: Source Code Retrieval using Semantic Application Traces

arXiv 2312.04731 ↗First posted 7 Dec 2023cs.SE cs.AI cs.IRcited by 0

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Abstract (arXiv)

Software reverse engineering is an essential task in software engineering and security, but it can be a challenging process, especially for adversarial artifacts. To address this challenge, we present STraceBERT, a novel approach that utilizes a Java dynamic analysis tool to record calls to core Java libraries, and pretrain a BERT-style model on the recorded application traces for effective method source code retrieval from a candidate set. Our experiments demonstrate the effectiveness of STraceBERT in retrieving the source code compared to existing approaches. Our proposed approach offers a promising solution to the problem of code retrieval in software reverse engineering and opens up new avenues for further research in this area.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 8 of its 17 references that are in the map. First posted on arXiv 7 Dec 2023.

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References · 8

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2021Evaluating Large Language Models Trained on Codecited by 8,320
  3. 2017Billion-scale similarity search with GPUscited by 2,497
  4. 2020CodeBERT: A Pre-Trained Model for Programming and Natural Languagescited by 1,310
  5. 2020CodeBLEU: a Method for Automatic Evaluation of Code Synthesiscited by 479
  6. 2019DIRE: A Neural Approach to Decompiled Identifier Namingcited by 36
  7. 2020Trex: Learning Execution Semantics from Micro-Traces for Binary Similaritycited by 34
  8. 2018BinMatch: A Semantics-based Hybrid Approach on Binary Code Clone Analysiscited by 2

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