Rahul Gupta


I am a graduate student at the Department of Computer Science & Automation, Indian Institute of Science, Bangalore. I work in Software Engineering and Analysis Lab (SEAL) with Dr. Aditya Kanade.

Research Interests

  • Artificial intelligence
  • Software engineering

Publications & Preprints

  • Deep Learning for Bug-Localization in Student Programs. PDF
    Rahul Gupta, Aditya Kanade, Shirish Shevade. arXiv 2019
    • Deep Reinforcement Learning for Syntactic Error Repair in Student Programs. PDF|PPT|Poster|Code
      Rahul Gupta, Aditya Kanade, Shirish Shevade. AAAI 2019
    • DeepFix: Fixing Common C Language Errors by Deep Learning. PDF|PPT|Code
      Rahul Gupta, Soham Pal, Aditya Kanade, Shirish Shevade. AAAI 2017
    • Partial Order Reduction for Event-driven Multi-threaded Programs. PDF|Code
      Pallavi Maiya, Rahul Gupta, Aditya Kanade, Rupak Majumdar. TACAS 2016

    Tools

    • RLAssist is an end-to-end deep reinforcement learning based programming language (PL) correction framework. This framework makes the PL correction task amenable to reinforcement learning. It also allows solving this task in an unsupervised manner, which was impossible with prior works.
    • DeepFix is an end to end deep learning based program repair framework for fixing common programming errors (CPEs). DeepFix is capable of localizing and fixing multiple CPEs in a given C program of a limited size. It was tested on thousands of student programming assignment submissions. The tests demonstrated that DeepFix fixed many programs while taking only a few tens of milliseconds per program.
    • EM-Explorer is a proof-of-concept explicit state model checking framework which simulates the Android concurrency semantics given an execution trace of an Android application. It is instantiated with implementations of Vector-clock versions of two Partial Order Reduction (POR) techniques, 1) Dynamic POR (DPOR),  and 2) EM-DPOR (TACAS 2016 paper). The framework was used to compare the above two POR techniques on various popular Android applications. It successfully showed that the latter is often orders of magnitude faster than the former.

    Teaching Assistance

    • E0239: Software Reliability Techniques. (Spring '16, '15)

    Contact Information

    Rahul Gupta
    PhD candidate
    Department of Computer Science & Automation,
    Indian Institute of Science, Bangalore - 560012
    email: rahulg on the iisc.ac.in domain


    Last updated on Wednesday, May 30, 2019.