The RAISE Lab (Real-world Artifical Intelligence for Software Engineering.)

Papers

Under Submission

• Wei Fu, Tim Menzies, Di Chen, Amritanshu Agrawal Building Better Quality Predictors Using “Epsilon-Dominance”

• Vivek Nair, Rahul Krishna, Tim Menzies, Pooyan Jamshidi “Transfer Learning with Bellwethers to find Good Configurations”

• Rahul Krishna, Suvodeep Majumder, Tim Menzies, Martin Shepperd “Bad Smells in Software Analytics Papers”

• Zhe Yu, Christopher Theisen, Hyunwoo Sohn, Laurie Williams, Tim Menzies “Cost-aware Vulnerability Prediction: the HARMLESS Approach”

• Zhe Yu and Tim Menzies “FAST2: a Better Text Miner for Faster Understanding of the SE Literature”

• Wunjie Wang, Mingyang Li, Song Wang, Tim Menzies and Qing Wang “Images Don’t Lie: Duplicate Crowdsourced Test Report Detection With Screenshot Information”

• Rahul Krishna and Tim Menzies. “Learning Effective Changes for Software Prjects” - Submitted to TOESM

• Vivek Nair, Zhe Yu and Tim Menzies. “FLASH: A Faster Optimizer for SBSE Tasks” arXiv preprint arXiv:1705.05018 (2017).

• Di Chen, Kathryn T. Stolee and Tim Menzies. “Replicating and Scaling up Qualitative Analysis using Crowdsourcing: A Github-based Case Study”

• Mitch Rees-Jones, Matthew Martin and Tim Menzies. “Better Predictors for Issue Lifetime”

• George Mathew, Tim Menzies and Jairus Hihn. “Impacts of Bad ESP (Early Size Predictions) on Software Effort Estimation” - Submitted to IST

• Morakot Choetkiertikul, Hoa Khanh Dam, Truyen Tran, Trang Pham, Aditya Ghose and Tim Menzies. “A deep learning model for estimating story points” arXiv preprint arXiv:1609.00489 (2016).

• Wei Fu, Vivek Nair, and Tim Menzies. “Why is Differential Evolution Better than Grid Search for Tuning Defect Predictors?” arXiv preprint arXiv:1609.02613 (2016). - Submitted to IST

Published

2018

• Di Chen, Wei Fu, Rahul Krishna, Tim Menzies “Applications of Psychological Science for Actionable Analytics” Accepted by FSE’18

• Chin-Jung Hsu, Vivek Nair, Tim Menzies, Vincent Freeh “Micky: A Cheaper Alternative for Selecting Cloud Instances” - Accepted by IEEE Cloud’2018

• Jianfeng Chen and Tim Menzies “RIOT: a Novel Stochastic Method for Rapidly Configuring Cloud-Based Workflows” - Accepted by IEEE Cloud’2018

• Majumder, Suvodeep, Nikhila Balaji, Katie Brey, Wei Fu, and Tim Menzies “500+ Times Faster Than Deep Learning (A Case Study Exploring Faster Methods for Text Mining StackOverflow).” arXiv preprint arXiv:1802.05319 (2018). - Accepted by MSR.

• Nair, Vivek, Amritanshu Agrawal, Jianfeng Chen, Wei Fu, George Mathew, Tim Menzies, Leandro Minku, Markus Wagner, and Zhe Yu. “Data-Driven Search-based Software Engineering.” arXiv preprint arXiv:1801.10241 (2018). - Accepted by MSR.

• Rahul Krishna and Tim Menzies. “Bellwethers: A Baseline Method For Transfer Learning” arXiv preprint arXiv:1703.06218 (2017). - Accepted by TSE.

• Agrawal, Amritanshu, Wei Fu, and Tim Menzies. “What is Wrong with Topic Modeling?(and How to Fix it Using Search-based SE).” arXiv preprint arXiv:1608.08176 (2016). - Accepted by IST

• Rahul Krishna, Amritanshu Agrawal, Akond Rahman, Alexander Sobran and Tim Menzies “What is the Connection Between Issues, Bugs, and Enhancements? (Lessons Learned from 800+ Software Projects)” arXiv preprint arXiv:1710.08736. - Accepted by ICSE SEIP’18

• Akond Rahman, Amritanshu Agrawal, Rahul Krishna, Alexander Sobran and Tim Menzies. “Continuous Integration: The Silver Bullet?” arXiv preprint arXiv:1711.03933 (2017) Accepted by ICSE SEIP’18.

• Amritanshu Agrawal, Akond Rahman, Rahul Krishna, Alexander Sobran and Tim Menzies “We Don’t Need Another Hero? The Impact of “Heroes” on Software Development” arXiv preprint arXiv:1710.09055. Accepted by ICSE SEIP’18.

2017

• Akond Rahman, Amritanshu Agrawal, Rahul Krishna, Alexander Sobran, Tim Menzies “Doing” Agile versus “Being” Agile. Empirical Results from 250+ Projects - ICSE SEIP 2018

• Amritanshu Agrawal and Tim Menzies. “Is “Better Data” Better than “Better Data Miners”? (On the Benefits of Tuning SMOTE for Defect Prediction)” - ICSE’18

• Jianfeng Chen, Vivek Nair, Rahul Krishna, and Tim Menzies. “Sampling as a Baseline Optimizer for Search-based Software Engineering” - TSE.

• Zhe Yu, Nicholas A. Kraft and Tim Menzies. “Finding Better Active Learners for Faster Literature Reviews “ - EMSE

• Jianfeng Chen, Vivek Nair, Tim Menzies. “Beyond Evolutionary Algorithms for Search-based Software Engineering” arXiv preprint arXiv:1701.07950 (2017). - Accepted for publication at IST.

• Rahul Krishna, Tim Menzies, and Lucas Layman. “Less is More: Minimizing Code Reorganization using XTREE” arXiv preprint arXiv:1609.03614 (2017). - Accepted for publication at IST.

• Krishna, Rahul. “Don’t Tell Me What Is, Tell Me What Ought To Be! Learning Effective Changes for Software Projects.” ASE’17 (Doctoral Symposium)

• Jaechang Nam, Wei Fu, Sunghun Kim, Tim Menzies, and Lin Tan. “Heterogeneous Defect Prediction”. - Accepted for publication in TSE

• Wei Fu, and Tim Menzies. “Revisiting Unsupervised Learning for Defect Prediction”. FSE ‘17

• Wei Fu, and Tim Menzies. “Easy over Hard: A Case Study on Deep Learning”. FSE ‘17

• Vivek Nair, Tim Menzies, Norbert Siegmund, and Sven Apel. “Using Bad Learners to find Good Configurations”. FSE ‘17

• George Mathew, Tim Menzies, Neil A. Ernst, and John Klein. “Shorter Reasoning About Larger Requirements Models”. RE ‘17

• George Mathew, Amritanshu Agrawal and Tim Menzies. “A Method for Finding Trends in Software Research”. TSE

• Tim Menzies, Ye Yang, George Mathew, Barry Boehm and Jairus Hihn. “Negative Results for Software Effort Estimation” - Empirical Software Engineering 22.5 (2017): 2658-2683.

• Tim Menzies, William Nichols, Forrest Shull and Lucas Layman. “Are Delayed Issues Harder to Resolve? Revisiting Cost-to-Fix of Defects throughout the Lifecycle” - Empirical Software Engineering 22.4 (2017): 1903-1935.

2016

• Yu, Zhe, and Tim Menzies. “Data Balancing for Technologically Assisted Reviews: Undersampling or Reweighting.” - CLEF’17

• Tim Menzies, Ye Yang, George Mathew, Jairus Hihn and Barry Boehm. “Negative Results for Software Effort Estimation.” Empirical Software Engineering. 2016.

• Fu, Wei, Tim Menzies, and Xipeng Shen. “Tuning for software analytics: Is it really necessary?.” Information and Software Technology. 2016.

• Rahul Krishna, Tim Menzies, and Wei Fu. “Too much automation? the bellwether effect and its implications for transfer learning.” Proceedings of the 31st IEEE/ACM International Conference on Automated Software Engineering. 2016.

• Nair, Vivek, Tim Menzies, and Jianfeng Chen. “An (Accidental) Exploration of Alternatives to Evolutionary Algorithms for SBSE”. Proceedings of the 8th International Symposium on Search-based Software Engineering. 2016

• Krishna, Rahul, Zhe Yu, Amritanshu Agrawal, Manuel Dominguez, and David Wolf “The BigSE project: lessons learned from validating industrial text mining.” Proceedings of the 2nd International Workshop on BIG Data Software Engineering. 2016.

2015

• Krishna, Rahul, and Tim Menzies. “Actionable= Cluster+ Contrast?.” 2015 30th IEEE/ACM International Conference on Automated Software Engineering Workshop (ASEW). IEEE, 2015.

• Menzies, Tim. “Cross-Project Data for Software Engineering.” Computer 48.12 (2015): 6-6.

• Hihn, Jaitus, and Tim Menzies. “Data Mining Methods and Cost Estimation Models: Why is it So Hard to Infuse New Ideas?.” 2015 30th IEEE/ACM International Conference on Automated Software Engineering Workshop (ASEW). IEEE, 2015.

• Partington, S. N., Menzies, T. J., Colburn, T. A., Saelens, B. E., & Glanz, “Reduced-Item Food Audits Based on the Nutrition Environment Measures Surveys.” American journal of preventive medicine 49.4 (2015): e23-e33.

• Bird, Christian, Tim Menzies, and Thomas Zimmermann, eds. The Art and Science of Analyzing Software Data. Elsevier, 2015.

• Menzies, Tim, and Corina Pasareanu. “Guest editorial: special multi-issue on selected topics in Automated Software Engineering.” Automated Software Engineering 22.3 (2015): 289-290.

• Harrison, Rachel, and Tim Menzies. “Guest editorial: special issue on realizing AI synergies in software engineering.” Automated Software Engineering 22.1 (2015): 1-2.

• Kocaguneli, Ekrem, Tim Menzies, and Emilia Mendes. “Transfer learning in effort estimation.” Empirical Software Engineering 20.3 (2015): 813-843.

• Menzies, Tim, Leandro Minku, and Fayola Peters. “The art and science of analyzing software data; quantitative methods.” 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering. Vol. 2. IEEE, 2015.

• Baresi, L., Menzies, T., Metzger, A., & Zimmermann, T. “1st international workshop on big data software engineering (BIGDSE 2015).” Proceedings of the 37th International Conference on Software Engineering-Volume 2. IEEE Press, 2015.

• Peters, Fayola, Tim Menzies, and Lucas Layman. “LACE2: better privacy-preserving data sharing for cross project defect prediction.” Proceedings of the 37th International Conference on Software Engineering-Volume 1. IEEE Press, 2015.

• Krall, Joseph, Tim Menzies, and Misty Davies. “Gale: Geometric active learning for search-based software engineering.” IEEE Transactions on Software Engineering 41.10 (2015): 1001-1018.