Rahul Krishna

Rahul Krishna

Post Doctoral Fellow; Machine Learning; Software Testing.

About Me

I am a Post doctoral fellow at Columbia University working ARiSE Lab with Dr. Baishakhi Ray (see rayb.info). In a nutshell, my current research explores the ways in which Machine Learning can be used to triggger, detect, and repair various problems in a software engineering pipeline.

I obtained my PhD form RAISE Lab at NC State University under the guidance of Dr. Tim Menzies (see menzies.us). During my PhD, I worked on actionable analytics for software engineering. I developed algorithms that go beyond prediction to generate insights that can assist decision making. I also worked on developing data mining techniques (transfer learning) to generate insights even when sufficient data is not available.

I am currently looking for academic and industrial positions. For more details, see

Selected Publications

Software testing

  • She, D., Krishna, R., Yan, L., Jana, S. and Ray, B., ‘‘MTFuzz: Fuzzing with a Multi-Task Neural Network.’’ In Intl. Conference on Foundations of Software Engineering (ECSE/FSE), 2020. https://arxiv.org/pdf/2005.12392.pdf

  • Wang, J., Yang, Y., Krishna, R., Menzies, T. & Wang, Q., “Effective Automated Decision Support for Managing Crowd testing”. Intl. Conference on Software Engineering (ICSE), 2019, Online: https://arxiv.org/pdf/1805.02744.pdf Best Paper Award

  • Chakraborty, S., Krishna, R., Ding, Y. and Ray, B., ‘‘Deep Learning based Vulnerability Detection: Are We There Yet?’’. In IEEE Transactions on Software Engineering (TSE), 2020 (Under review). Link: https://arxiv.org/abs/1703.06218

  • Chen, D., Fu, W., Krishna, R., & Menzies, T. “Applications of psychological science for actionable analytics”. Intl. Conference on Foundations of Software Engineering (ECSE/FSE), 2018. Online: https://arxiv.org/pdf/1803.05067

  • Krishna, R., Menzies, T., & Fu, W. “Too much automation? The Bellwether Effect and its Implications for Transfer Learning.” Intl. Conference on Automated Software Engineering (ASE), 2016. Online: https://doi.org/10.1145/2970276.2970339;

  • Krishna, R., Menzies, T., “Learning Actionable Analytics from Multiple Software Projects”. Empirical Software Engineering Journal (EMSE), 2019 . Online: https://arxiv.org/pdf/1708.05442.pdf;

  • Krishna, R. & Menzies, T., “Bellwethers: A Baseline Method For Transfer Learning”. In IEEE Transactions on Software Engineering (TSE), 2018. Online: https://arxiv.org/pdf/1703.06218;

Optimizing configurable systems

  • Krishna, R., Tang, C., Sullivan, K., Ray, B., “ConEx: Efficient Exploration of Big-Data System Configurations for Better Performance”. IEEE Transactions on Software Engineering (TSE), 2020. Online: https://arxiv.org/pdf/1910.09644.pdf;

  • Krishna, R., Nair, V., Jamshidi, P., & Menzies, T., “Whence to Learn? Transferring Knowledge in Configurable Systems using BEETLE.” IEEE Transactions on Software Engineering (TSE), 2020. https://arxiv.org/pdf/1910.09644.pdf;

  • Chen, J., Nair, V., Krishna, R., & Menzies, T. “Sampling as a Baseline Optimizer for Search-based Software Engineering”. IEEE Transactions on Software Engineering (TSE), 2018. Online: https://arxiv.org/pdf/1608.07617;

Industrial Collaboration

  • Krishna, R., Agrawal, A., Rahman, A., Sobran, A., & Menzies, T. “What is the Connection Between Issues, Bugs, and Enhancements? (Lessons Learned from 800+ Software Projects)”. Intl. Conf. Software Engineering, 2018 SEIP.** Online: https://arxiv.org/pdf/1710.08736;

  • Agrawal, A., Rahman, A., Krishna, R., Sobran, A. & Menzies, T. “We Don’t Need Another Hero? The Impact of ‘Heroes’ on Software Development”. Intl. Conf. Software Engineering, 2018 SEIP. Online: https://arxiv.org/pdf/1710.09055;

  • Rahman, A., Agrawal, A., Krishna, R., Sobran, A. & Menzies, T. “Characterizing The Influence of Continuous Integration. Empirical Results from 250+ Open Source and Proprietary Project”. Intl. Conference on Foundations of Software Engineering, SWAN 2018. Online: https://arxiv.org/pdf/1711.03933;

  • Krishna, R., Yu, Z., Agrawal, A., Dominguez, M., Wolf, D. “The ‘BigSE’ Project: Lessons Learned from Validating Industrial Text Mining. “. Intl. Workshop on Big Data Software Engineering (BIGDSE), 2016. Online: http://tiny.cc/BIGDSE16;