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PROCEEDINGS 2018 IEEE 11TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING (CLOUD), pp. 409–416. https://doi.org/10.1109/CLOUD.2018.00058 Chen, J., & Menzies, T. (2018). RIOT: a Stochastic-based Method for Workflow Scheduling in the Cloud. PROCEEDINGS 2018 IEEE 11TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING (CLOUD), pp. 318–325. https://doi.org/10.1109/CLOUD.2018.00047 Menzies, T. (2018). The Unreasonable Effectiveness of Software Analytics. IEEE SOFTWARE, 35(2), 96–98. https://doi.org/10.1109/ms.2018.1661323 Yu, Z., & Menzies, T. (2018). Total Recall, Language Processing, and Software Engineering. PROCEEDINGS OF THE 4TH ACM SIGSOFT INTERNATIONAL WORKSHOP ON NLP FOR SOFTWARE ENGINEERING (NL4SE '18), pp. 10–13. https://doi.org/10.1145/3283812.3283818 Agrawal, A., Fu, W., & Menzies, T. (2018). What is wrong with topic modeling? And how to fix it using search-based software engineering. INFORMATION AND SOFTWARE TECHNOLOGY, 98, 74–88. https://doi.org/10.1016/j.infsof.2018.02.005 Kessentini, M., & Menzies, T. (2017, September). A guest editorial: special issue on search based software engineering and data mining. AUTOMATED SOFTWARE ENGINEERING, Vol. 24, pp. 573–574. https://doi.org/10.1007/s10515-017-0217-2 Yang, Y., Falessi, D., Menzies, T., & Hihn, J. (2018). Actionable Analytics for Software Engineering INTRODUCTION. IEEE SOFTWARE, Vol. 35, pp. 51–53. https://doi.org/10.1109/ms.2017.4541039 Chen, J., Nair, V., & Menzies, T. (2018). Beyond evolutionary algorithms for search-based software engineering. INFORMATION AND SOFTWARE TECHNOLOGY, 95, 281–294. https://doi.org/10.1016/j.infsof.2017.08.007 Nair, V., Menzies, T., Siegmund, N., & Apel, S. (2018). Faster discovery of faster system configurations with spectral learning. AUTOMATED SOFTWARE ENGINEERING, 25(2), 247–277. https://doi.org/10.1007/s10515-017-0225-2 Nam, J., Fu, W., Kim, S., Menzies, T., & Tan, L. (2018). Heterogeneous Defect Prediction. IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, 44(9), 874–896. https://doi.org/10.1109/TSE.2017.2720603 Krishna, R., Menzies, T., & Layman, L. (2017). Less is more: Minimizing code reorganization using XTREE. INFORMATION AND SOFTWARE TECHNOLOGY, 88, 53–66. https://doi.org/10.1016/j.infsof.2017.03.012 Pandita, R., Jetley, R., Sudarsan, S., Menzies, T., & Williams, L. (2017). TMAP: Discovering relevant API methods through text mining of API documentation. Journal of Software: Evolution and Process, 29(12), e1845. https://doi.org/10.1002/SMR.1845 Hihn, J., Saing, M., Huntington, E., Johnson, J., Menzies, T., & Mathew, G. (2017). The NASA analogy software cost model: A web-based cost analysis tool. 2017 ieee aerospace conference. https://doi.org/10.1109/aero.2017.7943730 Nair, V., Menzies, T., Siegmund, N., & Apel, S. (2017). Using Bad Learners to Find Good Configurations. ESEC/FSE 2017: PROCEEDINGS OF THE 2017 11TH JOINT MEETING ON FOUNDATIONS OF SOFTWARE ENGINEERING, pp. 257–267. https://doi.org/10.1145/3106237.3106238 Prikladnicki, R., & Menzies, T. (2018). VOICE OF EVIDENCE From Voice of Evidence to Redirections. IEEE SOFTWARE, Vol. 35, pp. 11–13. https://doi.org/10.1109/ms.2017.4541053 Menzies, T. (2016). "How not to Do it": Anti-patterns for Data Science in Software Engineering. 2016 IEEE/ACM 38TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING COMPANION (ICSE-C), pp. 887–887. https://doi.org/10.1145/2889160.2891047 Nair, V., Menzies, T., & Chen, J. (2016). An (Accidental) Exploration of Alternatives to Evolutionary Algorithms for SBSE. SEARCH BASED SOFTWARE ENGINEERING, SSBSE 2016, Vol. 9962, pp. 96–111. https://doi.org/10.1007/978-3-319-47106-8_7 Menzies, T., Nichols, W., Shull, F., & Layman, L. (2017). Are delayed issues harder to resolve? Revisiting cost-to-fix of defects throughout the lifecycle. EMPIRICAL SOFTWARE ENGINEERING, 22(4), 1903–1935. https://doi.org/10.1007/s10664-016-9469-x Menzies, T. (2016). Correlation is not causation (or, when not to scream "Eureka!"). Perspectives on Data Science for Software Engineering, 327–330. https://doi.org/10.1016/b978-0-12-804206-9.00059-3 Hihn, J., Juster, L., Johnson, J., Menzies, T., & Michael, G. (2016). Improving and expanding NASA software cost estimation methods. 2016 ieee aerospace conference. https://doi.org/10.1109/aero.2016.7500655 Krall, J., Menzies, T., & Davies, M. (2016). Learning Mitigations for Pilot Issues When Landing Aircraft (via Multiobjective Optimization and Multiagent Simulations). IEEE TRANSACTIONS ON HUMAN-MACHINE SYSTEMS, 46(2), 221–230. https://doi.org/10.1109/thms.2015.2509980 Menzies, T., Yang, Y., Mathew, G., Boehm, B., & Hihn, J. (2017). Negative results for software effort estimation. EMPIRICAL SOFTWARE ENGINEERING, 22(5), 2658–2683. https://doi.org/10.1007/s10664-016-9472-2 Menzies, T., Williams, L., & Zimmermann, T. (2016). Perspectives on data science for software engineering. Perspectives on Data Science for Software Engineering, 3–6. https://doi.org/10.1016/b978-0-12-804206-9.00001-5 Menzies, T. (2016). Seven principles of inductive software engineering: What we do is different. Perspectives on Data Science for Software Engineering, 13–17. https://doi.org/10.1016/b978-0-12-804206-9.00003-9 Krishna, R., Menzies, T., & Fu, W. (2016). Too Much Automation? The Bellwether Effect and Its Implications for Transfer Learning. 2016 31ST IEEE/ACM INTERNATIONAL CONFERENCE ON AUTOMATED SOFTWARE ENGINEERING (ASE), pp. 122–131. https://doi.org/10.1145/2970276.2970339 Layman, L., Nikora, A. P., Meek, J., & Menzies, T. (2016). Topic Modeling of NASA Space System Problem Reports. 13TH WORKING CONFERENCE ON MINING SOFTWARE REPOSITORIES (MSR 2016), pp. 303–314. https://doi.org/10.1145/2901739.2901760 Fu, W., Menzies, T., & Shen, X. (2016). Tuning for software analytics: Is it really necessary? Information and Software Technology, 76, 135–146. https://doi.org/10.1016/j.infsof.2016.04.017 Baresi, L., Menzies, T., Metzger, A., & Zimmermann, T. (2015). 1st International Workshop on Big Data Software Engineering (BIGDSE 2015). 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering, Vol 2, pp. 965–966. https://doi.org/10.1109/icse.2015.308 Krishna, R., & Menzies, T. (2015). Actionable = Cluster plus Contrast? 2015 30TH IEEE/ACM INTERNATIONAL CONFERENCE ON AUTOMATED SOFTWARE ENGINEERING WORKSHOP (ASEW), pp. 14–17. https://doi.org/10.1109/asew.2015.23 Menzies, T. (2015, December). Cross-Project Data for Software Engineering. COMPUTER, Vol. 48, pp. 6–6. https://doi.org/10.1109/mc.2015.381 Hihn, J., & Menzies, T. (2015). 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), pp. 5–9. https://doi.org/10.1109/asew.2015.27 Krall, J., Menzies, T., & Davies, M. (2015). GALE: Geometric Active Learning for Search-Based Software Engineering. IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, 41(10), 1001–1018. https://doi.org/10.1109/tse.2015.2432024 Harrison, R., & Menzies, T. (2015, March). Guest editorial: special issue on realizing AI synergies in software engineering. AUTOMATED SOFTWARE ENGINEERING, Vol. 22, pp. 1–2. https://doi.org/10.1007/s10515-014-0174-y Harrison, R., & Menzies, T. (2015). Guest editorial: special issue on realizing AI synergies in software engineering (part 2). Automated Software Engineering, 22(2), 143–144. https://doi.org/10.1007/s10515-014-0177-8 Menzies, T., & Pasareanu, C. (2015, September). Guest editorial: special multi-issue on selected topics in Automated Software Engineering. AUTOMATED SOFTWARE ENGINEERING, Vol. 22, pp. 289–290. https://doi.org/10.1007/s10515-015-0180-8 Menzies, T., & Pasareanu, C. (2015). Guest editorial: special multi-issue on selected topics in automated software engineering. Automated Software Engineering, 22(4), 437–438. https://doi.org/10.1007/S10515-015-0181-7 Peters, F., Menzies, T., & Layman, L. (2015). LACE2: Better privacy-preserving data sharing for cross project defect prediction. 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering, Vol 1, 801–811. https://doi.org/10.1109/icse.2015.92 Partington, S. N., Menzies, T. J., Colburn, T. A., Saelens, B. E., & Glanz, K. (2015). Reduced-Item Food Audits Based on the Nutrition Environment Measures Surveys. American Journal of Preventive Medicine, 49(4), e23–e33. https://doi.org/10.1016/J.AMEPRE.2015.04.036 Menzies, T., Minku, L., & Peters, F. (2015). The Art and Science of Analyzing Software Data; Quantitative Methods. 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering, Vol 2, pp. 959–960. https://doi.org/10.1109/icse.2015.306 Partington, S., Murphy, E., Bowen, E., Lacombe, D., Piras, G., Cottrell, L., & Menzies, T. (2014). Choose to Change: The West Virginia Early Childhood Obesity Prevention Project. Journal of Nutrition Education and Behavior, 46(4), S197. https://doi.org/10.1016/J.JNEB.2014.04.213 Menzies, T., & Mernik, M. (2014). Special issue on realizing artificial intelligence synergies in software engineering. Software Quality Journal, 22(1), 49–50. https://doi.org/10.1007/S11219-014-9228-4 Kocaguneli, E., Menzies, T., & Mendes, E. (2015). Transfer learning in effort estimation. Empirical Software Engineering, 20(3), 813–843. https://doi.org/10.1007/S10664-014-9300-5 Partington, S., Murphy, E., Bowen, E., Lacombe, D., Piras, G., Carson, L., … Menzies, T. (2013). Choose to Change: The West Virginia Early Childhood Obesity Prevention Project. Journal of Nutrition Education and Behavior, 45(4), S92. https://doi.org/10.1016/J.JNEB.2013.04.271 Menzies, T. (2013). Guest editorial for the Special Section on BEST PAPERS from the 2011 conference on Predictive Models in Software Engineering (PROMISE). Information and Software Technology, 55(8), 1477–1478. https://doi.org/10.1016/J.INFSOF.2013.03.006 Menzies, T., & Koru, G. (2013). Predictive models in software engineering. Empirical Software Engineering, 18(3), 433–434. https://doi.org/10.1007/S10664-013-9252-1 Kocaguneli, E., & Menzies, T. (2013). Software effort models should be assessed via leave-one-out validation. Journal of Systems and Software, 86(7), 1879–1890. https://doi.org/10.1016/J.JSS.2013.02.053 Kim, Y. S., Kang, B. H., Ryu, S. H., Compton, P., Han, S. C., & Menzies, T. (2012). Crowd-Sourced Knowledge Bases. In Knowledge Management and Acquisition for Intelligent Systems (pp. 258–271). https://doi.org/10.1007/978-3-642-32541-0_23 Keung, J., Kocaguneli, E., & Menzies, T. (2012). Finding conclusion stability for selecting the best effort predictor in software effort estimation. Automated Software Engineering, 20(4), 543–567. https://doi.org/10.1007/S10515-012-0108-5 Menzies, T., & Shepperd, M. (2012). Special issue on repeatable results in software engineering prediction. Empirical Software Engineering, 17(1-2), 1–17. https://doi.org/10.1007/S10664-011-9193-5 Bener, A., & Menzies, T. (2011). Guest editorial: learning to organize testing. Automated Software Engineering, 19(2), 137–140. https://doi.org/10.1007/S10515-011-0095-Y Kocaguneli, E., Menzies, T., & Keung, J. W. (2011). Kernel methods for software effort estimation. Empirical Software Engineering, 18(1), 1–24. https://doi.org/10.1007/S10664-011-9189-1 Nandeshwar, A., Menzies, T., & Nelson, A. (2011). Learning patterns of university student retention. Expert Systems with Applications, 38(12), 14984–14996. https://doi.org/10.1016/j.eswa.2011.05.048 El-Rawas, O., & Menzies, T. (2010). A second look at Faster, Better, Cheaper. Innovations in Systems and Software Engineering, 6(4), 319–335. https://doi.org/10.1007/S11334-010-0137-9 Gay, G., Menzies, T., Davies, M., & Gundy-Burlet, K. (2010). Automatically finding the control variables for complex system behavior. Automated Software Engineering, 17(4), 439–468. https://doi.org/10.1007/s10515-010-0072-x Menzies, T., Milton, Z., Turhan, B., Cukic, B., Jiang, Y., & Bener, A. (2010). Defect prediction from static code features: current results, limitations, new approaches. Automated Software Engineering, 17(4), 375–407. https://doi.org/10.1007/s10515-010-0069-5 Tosun, A., Bener, A., Turhan, B., & Menzies, T. (2010). Practical considerations in deploying statistical methods for defect prediction: A case study within the Turkish telecommunications industry. Information and Software Technology, 52(11), 1242–1257. https://doi.org/10.1016/j.infsof.2010.06.006 Turhan, B., Bener, A., & Menzies, T. (2010). Regularities in Learning Defect Predictors. In Product-Focused Software Process Improvement (pp. 116–130). https://doi.org/10.1007/978-3-642-13792-1_11 Nelson, A., Menzies, T., & Gay, G. (2010). Sharing experiments using open-source software. Software: Practice and Experience, 41(3), 283–305. https://doi.org/10.1002/spe.1004 Menzies, T., Jalali, O., Hihn, J., Baker, D., & Lum, K. (2010). Stable rankings for different effort models. Automated Software Engineering, 17(4), 409–437. https://doi.org/10.1007/s10515-010-0070-z Menzies, T., Williams, S., Elrawas, O., Baker, D., Boehm, B., Hihn, J., … Madachy, R. (2009). Accurate estimates without local data? Software Process: Improvement and Practice, 14(4), 213–225. https://doi.org/10.1002/spip.414 Gay, G., Menzies, T., Jalali, O., Mundy, G., Gilkerson, B., Feather, M., & Kiper, J. (2009). Finding robust solutions in requirements models. Automated Software Engineering, 17(1), 87–116. https://doi.org/10.1007/s10515-009-0059-7 Orrego, A., Menzies, T., & El-Rawas, O. (2009). On the Relative Merits of Software Reuse. In Trustworthy Software Development Processes (pp. 186–197). https://doi.org/10.1007/978-3-642-01680-6_18 Turhan, B., Menzies, T., Bener, A. B., & Di Stefano, J. (2009). On the relative value of cross-company and within-company data for defect prediction. Empirical Software Engineering, 14(5), 540–578. https://doi.org/10.1007/s10664-008-9103-7 Menzies, T., Elrawas, O., Boehm, B., Madachy, R., Hihn, J., Baker, D., & Lum, K. (2008). Accurate Estimates without Calibration? In Making Globally Distributed Software Development a Success Story (pp. 210–221). https://doi.org/10.1007/978-3-540-79588-9_19 Menzies, T. (2008). Editorial, special issue, repeatable experiments in software engineering. Empirical Software Engineering, 13(5), 469–471. https://doi.org/10.1007/s10664-008-9087-3 Menzies, T., Benson, M., Costello, K., Moats, C., Northey, M., & Richardson, J. (2008). Learning better IV&V practices. Innovations in Systems and Software Engineering, 4(2), 169–183. https://doi.org/10.1007/S11334-008-0046-3 Etzkorn, L., & Menzies, T. (2008). Special issue on information retrieval for program comprehension. Empirical Software Engineering, 14(1), 1–4. https://doi.org/10.1007/s10664-008-9097-1 Menzies, T., & Hu, Y. (2006). Just enough learning (of association rules): the TAR2 “Treatment” learner. Artificial Intelligence Review, 25(3), 211–229. https://doi.org/10.1007/s10462-007-9055-0 Menzies, T. (2004). Mining repositories to assist in project planning and resource allocation. "International Workshop on Mining Software Repositories (MSR 2004)" W17S Workshop - 26th International Conference on Software Engineering. Presented at the "International Workshop on Mining Software Repositories (MSR 2004)" W17S Workshop - 26th International Conference on Software Engineering. https://doi.org/10.1049/IC:20040480