Debanjan Chatterjee
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Debanjan Chatterjee has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Amey Karkare
Indian Institute of Technology Kanpur
2 shared publications - Fahad Shaikh
IIT Kanpur, India
2 shared publications - Mayank Bansal
IIT Kanpur, India
2 shared publications - Preeti Singh
IIT Kanpur, India
2 shared publications - Purushottam Kar
Department of Computer Science and Engineering, Indian Institute of Technology Kanpur, Kanpur, 208016, India
2 shared publications - Sharath H. Padmanabha
IIT Kanpur, India
2 shared publications
Latest Publications
- Open Access
- 23 February 2023
- Published by Association for Computing Machinery (ACM)
- p. 1-10
PRIORITY: An Intelligent Problem Indicator Repository
- 23 February 2023
- Published by Association for Computing Machinery (ACM)
- p. 1-10
Open AccessPRIORITY: An Intelligent Problem Indicator Repository
- Open Access
- 23 February 2023
- Published by Association for Computing Machinery (ACM)
- p. 1-11
Advances in Automated Pedagogical Compile-time Error Repair
Automated pedagogical error repair (APER) is the task of suggesting fixes to buggy programs written by beginner or novice programmers. APER tools have been shown to greatly improve the learning experience for students for whom error messages offered by compilers or runtime environments are either unhelpful and often misleading. Consequently, several APER tools have been proposed in literature using a variety of powerful machine learning techniques including sequence-to-sequence modelling (TRACER), reinforcement learning (RLAssist), graph attention (DrRepair) and decision trees (MACER). Despite offering high repair rates, these tools are often bulky, requiring several days of training and extensive GPU resources. This paper describes CAPER, a novel APER tool for the C programming language that offers 4-5% higher repair accuracy than existing APER tools on multiple benchmark error repair datasets. CAPER has the added advantage of being significantly lighter and faster than most existing tools, being able to train on a CPU and yet offering training speedups of 2 × over TRACER, 600 × over RLAssist and 700 × over DrRepair. The paper also describes PyPER, an extension of CAPER to the Python programming language. Code for CAPER is available at https://github.com/purushottamkar/caper/
- 23 February 2023
- Published by Association for Computing Machinery (ACM)
- p. 1-11
Open AccessAdvances in Automated Pedagogical Compile-time Error Repair
Automated pedagogical error repair (APER) is the task of suggesting fixes to buggy programs written by beginner or novice programmers. APER tools have been shown to greatly improve the learning experience for students for whom error messages offered by compilers or runtime environments are either unhelpful and often misleading. Consequently, several APER tools have been proposed in literature using a variety of powerful machine learning techniques including sequence-to-sequence modelling (TRACER), reinforcement learning (RLAssist), graph attention (DrRepair) and decision trees (MACER). Despite offering high repair rates, these tools are often bulky, requiring several days of training and extensive GPU resources. This paper describes CAPER, a novel APER tool for the C programming language that offers 4-5% higher repair accuracy than existing APER tools on multiple benchmark error repair datasets. CAPER has the added advantage of being significantly lighter and faster than most existing tools, being able to train on a CPU and yet offering training speedups of 2 × over TRACER, 600 × over RLAssist and 700 × over DrRepair. The paper also describes PyPER, an extension of CAPER to the Python programming language. Code for CAPER is available at https://github.com/purushottamkar/caper/