Pooja Rani

I am a PhD Candidate at the University of Bern in the Software Composition Group since January 2018. Prior to this, I have done masters in software system from the Birla Institute of Technology and Science-Pilani During my masters, I have worked on “Software Fault Tree Analysis” which is a safety analysis technique to identify a software-related error in early design phase from UML model.

Research Interest

As the complexity of software system increase, it becomes more important to tackle the future problem in the early stages. To support developers in building these complex systems we require tools which can support rapid development, understanding the system, re-using the existing system, reduce testing effort, and automate various tasks for them. My research interest lies in supporting developers with various tools in their IDEs. Currently I am working on gathering and identifying developers information needs regarding code comments for program comprehensions tasks.

Developing smart IDEs Object-Oriented Programming
Improving code documentation Code comments analysis
Mining repositories

Project Proposals

Finished Thesis projects

Past Proposals

Teaching Activities

Contact Details

Email: pooja.rani |at| inf.unibe.ch
Telephone: +41 31 511 7639
Address: University of Bern, Software Composition Group,
Room 106, Schützenmattstrasse 14, CH-3012 Bern
Webpage: http://scg.unibe.ch/staff/Pooja-Rani

Publications

2021

  1. Pooja Rani. Speculative Analysis for Quality Assessment of Code Comments. In 2021 IEEE/ACM 43rd International Conference on Software Engineering: Companion Proceedings (ICSE-Companion), p. 299-303, 2021. DOI PDF 
  2. Pooja Rani, Sebastiano Panichella, Manuel Leuenberger, Mohammad Ghafari, and Oscar Nierstrasz. What do class comments tell us? An investigation of comment evolution and practices in Pharo Smalltalk. In Empirical Software Engineering 26(6) p. 1—49, 2021. DOI PDF 
  3. Mathias Birrer, Pooja Rani, Sebastiano Panichella, and Oscar Nierstrasz. Makar: A Framework for Multi-source Studies based on Unstructured Data. In 2021 IEEE International Conference on Software Analysis, Evolution and Reengineering (SANER), p. 577-581, 2021. DOI PDF 
  4. Pooja Rani, Sebastiano Panichella, Manuel Leuenberger, Andrea Di Sorbo, and Oscar Nierstrasz. How to Identify Class Comment Types? A Multi-language Approach for Class Comment Classification. In Journal of Systems and Software 181 p. 111047, 2021. DOI PDF 
  5. Pooja Rani, Mathias Birrer, Sebastiano Panichella, Mohammad Ghafari, and Oscar Nierstrasz. What Do Developers Discuss about Code Comments?. In 2021 IEEE 21st International Working Conference on Source Code Analysis and Manipulation (SCAM), 2021. PDF 
  6. Pooja Rani, Suada Abukar, Nataliia Stulova, Alexander Bergel, and Oscar Nierstrasz. Do Comments follow Commenting Conventions? A Case Study in Java and Python. In 2021 IEEE 21st International Working Conference on Source Code Analysis and Manipulation (SCAM), 2021. PDF 

2018

  1. Pooja Rani. Software Analysis using Natural Language Queries. In Seminar Series on Advanced Techniques \& Tools for Software Evolution (SATToSE), 2018. PDF 

Last changed by pooja on 17 September 2021