Timothy Menzies

Johnson, B., & Menzies, T. (2024). Ethics: Why Software Engineers Can't Afford to Look Away. IEEE SOFTWARE, 41(1), 142–144. https://doi.org/10.1109/MS.2023.3319768

Johnson, B., & Menzies, T. (2024). Fighting for What's Right: An Interview With Marc Canellas. IEEE SOFTWARE, 41(2), 104–107. https://doi.org/10.1109/MS.2023.3340928

Lustosa, A., & Menzies, T. (2024). Learning from Very Little Data: On the Value of Landscape Analysis for Predicting Software Project Health. ACM Transactions on Software Engineering and Methodology. https://doi.org/10.1145/3630252

Johnson, B., & Menzies, T. (2024). The Power of Positionality-Why Accessibility? An Interview With Kevin Moran and Arun Krishnavajjala. IEEE SOFTWARE, 41(3), 91–94. https://doi.org/10.1109/MS.2024.3360650

Ling, X., Menzies, T., Hazard, C., Shu, J., & Beel, J. (2024). Trading Off Scalability, Privacy, and Performance in Data Synthesis. IEEE ACCESS, 12, 26642–26654. https://doi.org/10.1109/ACCESS.2024.3366556

Majumder, S., Chakraborty, J., & Menzies, T. (2024). When less is more: on the value of "co-training" for semi-supervised software defect predictors. EMPIRICAL SOFTWARE ENGINEERING, 29(2). https://doi.org/10.1007/s10664-023-10418-4

Menzies, T., & Hazard, C. (2023, September). "The Best Data Are Fake Data?": An Interview With Chris Hazard. IEEE SOFTWARE, Vol. 40, pp. 121–124. https://doi.org/10.1109/MS.2023.3286480

Baldassarre, M. T., Ernst, N., Hermann, B., Menzies, T., & Yedida, R. (2023). (Re)Use of Research Results (Is Rampant). COMMUNICATIONS OF THE ACM, 66(2), 75–81. https://doi.org/10.1145/3554976

Zhang, G., Sun, J., Xu, F., Sui, Y., Bandara, H. M. N. D., Chen, S., & Menzies, T. (2023). A Tale of Two Cities: Data and Configuration Variances in Robust Deep Learning. IEEE INTERNET COMPUTING, 27(6), 13–20. https://doi.org/10.1109/MIC.2023.3322283

Yedida, R., Krishna, R., Kalia, A., Menzies, T., Xiao, J., & Vukovic, M. (2023). An expert system for redesigning software for cloud applications. EXPERT SYSTEMS WITH APPLICATIONS, 219. https://doi.org/10.1016/j.eswa.2023.119673

Shrikanth, N. C., & Menzies, T. (2023). Assessing the Early Bird Heuristic (for Predicting ProjectQuality). ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY, 32(5). https://doi.org/10.1145/3583565

Alvarez, L., & Menzies, T. (2023). Don't Lie to Me: Avoiding Malicious Explanations With STEALTH. IEEE SOFTWARE, 40(3), 43–53. https://doi.org/10.1109/MS.2023.3244713

Majumder, S., Chakraborty, J., Bai, G. R., Stolee, K. T., & Menzies, T. (2023). Fair Enough: Searching for Sufficient Measures of Fairness. ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY, 32(6). https://doi.org/10.1145/3585006

Peng, K., Chakraborty, J., & Menzies, T. (2023). FairMask: Better Fairness via Model-Based Rebalancing of Protected Attributes. IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, 49(4), 2426–2439. https://doi.org/10.1109/TSE.2022.3220713

Mathew, G., Agrawal, A., & Menzies, T. (2023). Finding Trends in Software Research. IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, 49(4), 1397–1410. https://doi.org/10.1109/TSE.2018.2870388

Menzies, T. (2023). How to "Sell" Ethics (Using AI): An Interview With Alexander Serebrenik. IEEE SOFTWARE, 40(3), 95–97. https://doi.org/10.1109/MS.2023.3249539

Yedida, R., Kang, H. J., Tu, H., Yang, X., Lo, D., & Menzies, T. (2023). How to Find Actionable Static Analysis Warnings: A Case Study With FindBugs. IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, 49(4), 2856–2872. https://doi.org/10.1109/TSE.2023.3234206

Menzies, T., Johnson, B. L., Roberts, D., & Alvarez, L. (2023). The Engineering Mindset Is an Ethical Mindset (We Just Don't Teach It That Way ... Yet). IEEE SOFTWARE, Vol. 40, pp. 103–110. https://doi.org/10.1109/MS.2022.3227597

Johnson, B., & Menzies, T. (2023). Unfairness Is Everywhere, so What to Do? An Interview With Jeanna Matthews. IEEE SOFTWARE, 40(6), 135–138. https://doi.org/10.1109/MS.2023.3305722

Peng, K., Kaltenecker, C., Siegmund, N., Apel, S., & Menzies, T. (2023). VEER: enhancing the interpretability of model-based optimizations. EMPIRICAL SOFTWARE ENGINEERING, 28(3). https://doi.org/10.1007/s10664-023-10296-w

Ling, X., & Menzies, T. (2023). What Not to Test (For Cyber-Physical Systems). IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, 49(7), 3811–3826. https://doi.org/10.1109/TSE.2023.3272309

Mashkoor, A., Menzies, T., Egyed, A., & Ramler, R. (2022). Artificial Intelligence and Software Engineering: Are We Ready? COMPUTER, 55(3), 24–28. https://doi.org/10.1109/MC.2022.3144805

Yu, Z., Carver, J. C., Rothermel, G., & Menzies, T. (2022). Assessing expert system-assisted literature reviews with a case study. EXPERT SYSTEMS WITH APPLICATIONS, 200. https://doi.org/10.1016/j.eswa.2022.116958

Tu, H., Yu, Z., & Menzies, T. (2022). Better Data Labelling With EMBLEM (and how that Impacts Defect Prediction). IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, 48(1), 278–294. https://doi.org/10.1109/TSE.2020.2986415

Shu, R., Xia, T., Williams, L., & Menzies, T. (2022). Dazzle: Using Optimized Generative Adversarial Networks to Address Security Data Class Imbalance Issue. 2022 MINING SOFTWARE REPOSITORIES CONFERENCE (MSR 2022), pp. 144–155. https://doi.org/10.1145/3524842.3528437

Peng, K., & Menzies, T. (2022). Defect Reduction Planning (Using TimeLIME). IEEE Transactions on Software Engineering, 48(7), 2510–2525. https://doi.org/10.1109/TSE.2021.3062968

Elder, S., Zahan, N., Shu, R., Metro, M., Kozarev, V., Menzies, T., & Williams, L. (2022). Do I really need all this work to find vulnerabilities? An empirical case study comparing vulnerability detection techniques on a Java application. EMPIRICAL SOFTWARE ENGINEERING, 27(6). https://doi.org/10.1007/s10664-022-10179-6

Ling, X., Agrawal, R., & Menzies, T. (2022). How Different is Test Case Prioritization for Open and Closed Source Projects? IEEE Transactions on Software Engineering, 48(7), 2526–2540. https://doi.org/10.1109/TSE.2021.3063220

Yedida, R., & Menzies, T. (2022). How to Improve Deep Learning for Software Analytics (a case study with code smell detection). 2022 MINING SOFTWARE REPOSITORIES CONFERENCE (MSR 2022), pp. 156–166. https://doi.org/10.1145/3524842.3528458

Yu, Z., Fahid, F. M., Tu, H., & Menzies, T. (2022). Identifying Self-Admitted Technical Debts With Jitterbug: A Two-Step Approach. IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, 48(5), 1676–1691. https://doi.org/10.1109/TSE.2020.3031401

Majumder, S., Xia, T., Krishna, R., & Menzies, T. (2022). Methods for Stabilizing Models Across Large Samples of Projects (with case studies on Predicting Defect and Project Health). 2022 MINING SOFTWARE REPOSITORIES CONFERENCE (MSR 2022), pp. 566–578. https://doi.org/10.1145/3524842.3527934

Shu, R., Xia, T., Williams, L., & Menzies, T. (2022). Omni: automated ensemble with unexpected models against adversarial evasion attack. EMPIRICAL SOFTWARE ENGINEERING, 27(1). https://doi.org/10.1007/s10664-021-10064-8

Xia, T., Fu, W., Shu, R., Agrawal, R., & Menzies, T. (2022). Predicting health indicators for open source projects (using hyperparameter optimization). EMPIRICAL SOFTWARE ENGINEERING, 27(6). https://doi.org/10.1007/s10664-022-10171-0

Majumder, S., Mody, P., & Menzies, T. (2022). Revisiting process versus product metrics: a large scale analysis. EMPIRICAL SOFTWARE ENGINEERING, 27(3). https://doi.org/10.1007/s10664-021-10068-4

Xia, T., Shu, R., Shen, X., & Menzies, T. (2022). Sequential Model Optimization for Software Effort Estimation. IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, 48(6), 1994–2009. https://doi.org/10.1109/TSE.2020.3047072

Bencomo, N., Guo, J. L. C., Harrison, R., Heyn, H.-M., & Menzies, T. (2022, January). The Secret to Better AI and Better Software (Is Requirements Engineering). IEEE SOFTWARE, Vol. 39, pp. 105–110. https://doi.org/10.1109/MS.2021.3118099

Shrikanth, N. C., Nichols, W., Fahid, F. M., & Menzies, T. (2021). Assessing practitioner beliefs about software engineering. EMPIRICAL SOFTWARE ENGINEERING, 26(4). https://doi.org/10.1007/s10664-021-09957-5

Chakraborty, J., Majumder, S., & Menzies, T. (2021). Bias in Machine Learning Software: Why? How? What to Do? PROCEEDINGS OF THE 29TH ACM JOINT MEETING ON EUROPEAN SOFTWARE ENGINEERING CONFERENCE AND SYMPOSIUM ON THE FOUNDATIONS OF SOFTWARE ENGINEERING (ESEC/FSE '21), pp. 429–440. https://doi.org/10.1145/3468264.3468537

Wang, J., Wang, S., Chen, J., Menzies, T., Cui, Q., Xie, M., & Wang, Q. (2021). Characterizing Crowds to Better Optimize Worker Recommendation in Crowdsourced Testing. IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, 47(6), 1259–1276. https://doi.org/10.1109/TSE.2019.2918520

Yang, X., & Menzies, T. (2021). Documenting Evidence of a Replication of 'Analyze This! 145 Questions for Data Scientists in Software Engineering'. PROCEEDINGS OF THE 29TH ACM JOINT MEETING ON EUROPEAN SOFTWARE ENGINEERING CONFERENCE AND SYMPOSIUM ON THE FOUNDATIONS OF SOFTWARE ENGINEERING (ESEC/FSE '21), pp. 1602–1602. https://doi.org/10.1145/3468264.3477219

Yang, X., & Menzies, T. (2021). Documenting Evidence of a Replication of 'Populating a Release History Database from Version Control and Bug Tracking Systems'. PROCEEDINGS OF THE 29TH ACM JOINT MEETING ON EUROPEAN SOFTWARE ENGINEERING CONFERENCE AND SYMPOSIUM ON THE FOUNDATIONS OF SOFTWARE ENGINEERING (ESEC/FSE '21), pp. 1601–1601. https://doi.org/10.1145/3468264.3477218

Yang, X., & Menzies, T. (2021). Documenting Evidence of a Reproduction of Is There A "Golden" Feature Set for Static Warning Identification? - An Experimental Evaluation'. PROCEEDINGS OF THE 29TH ACM JOINT MEETING ON EUROPEAN SOFTWARE ENGINEERING CONFERENCE AND SYMPOSIUM ON THE FOUNDATIONS OF SOFTWARE ENGINEERING (ESEC/FSE '21), pp. 1603–1603. https://doi.org/10.1145/3468264.3477220

Peng, K., & Menzies, T. (2021). Documenting Evidence of a Reuse of "'Why Should I Trust You?": Explaining the Predictions of Any Classifier'. PROCEEDINGS OF THE 29TH ACM JOINT MEETING ON EUROPEAN SOFTWARE ENGINEERING CONFERENCE AND SYMPOSIUM ON THE FOUNDATIONS OF SOFTWARE ENGINEERING (ESEC/FSE '21), pp. 1600–1600. https://doi.org/10.1145/3468264.3477217

Lustosa, A., & Menzies, T. (2021). Documenting Evidence of a Reuse of 'A Systematic Literature Review of Techniques and Metrics to Reduce the Cost of Mutation Testing'. PROCEEDINGS OF THE 29TH ACM JOINT MEETING ON EUROPEAN SOFTWARE ENGINEERING CONFERENCE AND SYMPOSIUM ON THE FOUNDATIONS OF SOFTWARE ENGINEERING (ESEC/FSE '21), pp. 1597–1597. https://doi.org/10.1145/3468264.3477214

Yedida, R., & Menzies, T. (2021). Documenting Evidence of a Reuse of 'A Systematic Study of the Class Imbalance Problem in Convolutional Neural Networks'. PROCEEDINGS OF THE 29TH ACM JOINT MEETING ON EUROPEAN SOFTWARE ENGINEERING CONFERENCE AND SYMPOSIUM ON THE FOUNDATIONS OF SOFTWARE ENGINEERING (ESEC/FSE '21), pp. 1595–1595. https://doi.org/10.1145/3468264.3477212

Yedida, R., & Menzies, T. (2021). Documenting Evidence of a Reuse of 'On the Number of Linear Regions of Deep Neural Networks'. PROCEEDINGS OF THE 29TH ACM JOINT MEETING ON EUROPEAN SOFTWARE ENGINEERING CONFERENCE AND SYMPOSIUM ON THE FOUNDATIONS OF SOFTWARE ENGINEERING (ESEC/FSE '21), pp. 1596–1596. https://doi.org/10.1145/3468264.3477213

Lustosa, A., & Menzies, T. (2021). Documenting Evidence of a Reuse of 'RefactoringMiner 2.0'. PROCEEDINGS OF THE 29TH ACM JOINT MEETING ON EUROPEAN SOFTWARE ENGINEERING CONFERENCE AND SYMPOSIUM ON THE FOUNDATIONS OF SOFTWARE ENGINEERING (ESEC/FSE '21), pp. 1598–1598. https://doi.org/10.1145/3468264.3477215

Peng, K., & Menzies, T. (2021). Documenting Evidence of a Reuse of 'What is a Feature? A Qualitative Study of Features in Industrial Software Product Lines'. PROCEEDINGS OF THE 29TH ACM JOINT MEETING ON EUROPEAN SOFTWARE ENGINEERING CONFERENCE AND SYMPOSIUM ON THE FOUNDATIONS OF SOFTWARE ENGINEERING (ESEC/FSE '21), pp. 1599–1599. https://doi.org/10.1145/3468264.3477216

Shrikanth, N. C., Majumder, S., & Menzies, T. (2021). Early Life Cycle Software Defect Prediction. Why? How? 2021 IEEE/ACM 43RD INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING (ICSE 2021), pp. 448–459. https://doi.org/10.1109/ICSE43902.2021.00050

Tu, H., & Menzies, T. (2021). FRUGAL: Unlocking Semi-Supervised Learning for Software Analytics. 2021 36TH IEEE/ACM INTERNATIONAL CONFERENCE ON AUTOMATED SOFTWARE ENGINEERING ASE 2021, pp. 394–406. https://doi.org/10.1109/ASE51524.2021.9678617

Shu, R., Xia, T., Chen, J., Williams, L., & Menzies, T. (2021). How to Better Distinguish Security Bug Reports (Using Dual Hyperparameter Optimization). EMPIRICAL SOFTWARE ENGINEERING, 26(3). https://doi.org/10.1007/s10664-020-09906-8

Yu, Z., Theisen, C., Williams, L., & Menzies, T. (2021). Improving Vulnerability Inspection Efficiency Using Active Learning. IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, 47(11), 2401–2420. https://doi.org/10.1109/TSE.2019.2949275

Yang, X., Chen, J., Yedida, R., Yu, Z., & Menzies, T. (2021). Learning to recognize actionable static code warnings (is intrinsically easy). EMPIRICAL SOFTWARE ENGINEERING, 26(3). https://doi.org/10.1007/s10664-021-09948-6

Tu, H., Papadimitriou, G., Kiran, M., Wang, C., Mandal, A., Deelman, E., & Menzies, T. (2021). Mining Workflows for Anomalous Data Transfers. 2021 IEEE/ACM 18TH INTERNATIONAL CONFERENCE ON MINING SOFTWARE REPOSITORIES (MSR 2021), pp. 1–12. https://doi.org/10.1109/MSR52588.2021.00013

Yedida, R., & Menzies, T. (2021). On the Value of Oversampling for Deep Learning in Software Defect Prediction. IEEE Transactions on Software Engineering, 48(8), 1–1. https://doi.org/10.1109/TSE.2021.3079841

Menzies, T. (2021). Shockingly Simple: "Keys" for Better AI for SE. IEEE SOFTWARE, Vol. 38, pp. 114–118. https://doi.org/10.1109/MS.2020.3043014

Agrawal, A., Yang, X., Agrawal, R., Yedida, R., Shen, X., & Menzies, T. (2021). Simpler Hyperparameter Optimization for Software Analytics: Why, How, When. IEEE Transactions on Software Engineering, 48(8), 1–1. https://doi.org/10.1109/TSE.2021.3073242

Elder, S. E., Zahan, N., Kozarev, V., Shu, R., Menzies, T., & Williams, L. (2021). Structuring a Comprehensive Software Security Course Around the OWASP Application Security Verification Standard. 2021 IEEE/ACM 43RD INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING: JOINT TRACK ON SOFTWARE ENGINEERING EDUCATION AND TRAINING (ICSE-JSEET 2021), pp. 95–104. https://doi.org/10.1109/ICSE-SEET52601.2021.00019

Yang, X., Yu, Z., Wang, J., & Menzies, T. (2021). Understanding static code warnings: An incremental AI approach. EXPERT SYSTEMS WITH APPLICATIONS, 167. https://doi.org/10.1016/j.eswa.2020.114134

Krishna, R., Nair, V., Jamshidi, P., & Menzies, T. (2021). Whence to Learn? Transferring Knowledge in Configurable Systems Using BEETLE. IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, 47(12), 2956–2972. https://doi.org/10.1109/TSE.2020.2983927

Shrikanth, N. C., & Menzies, T. (2020). Assessing Practitioner Beliefs about Software Defect Prediction. 2020 IEEE/ACM 42ND INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING: SOFTWARE ENGINEERING IN PRACTICE (ICSE-SEIP), pp. 182–190. https://doi.org/10.1145/3377813.3381367

Agrawal, A., Menzies, T., Minku, L. L., Wagner, M., & Yu, Z. (2020). Better software analytics via "DUO": Data mining algorithms using/used-by optimizers. EMPIRICAL SOFTWARE ENGINEERING, 25(3), 2099–2136. https://doi.org/10.1007/s10664-020-09808-9

Carleton, A. D., Harper, E., Lyu, M. R., Eldh, S., Xie, T., & Menzies, T. (2020). Expert Perspectives on AI. IEEE SOFTWARE, Vol. 37, pp. 87–94. https://doi.org/10.1109/MS.2020.2987673

Nair, V., Yu, Z., Menzies, T., Siegmund, N., & Apel, S. (2020). Finding Faster Configurations Using FLASH. IEEE Transactions on Software Engineering, 46(7), 794–811. https://doi.org/10.1109/TSE.2018.2870895

Krishna, R., & Menzies, T. (2020). Learning actionable analytics from multiple software projects. EMPIRICAL SOFTWARE ENGINEERING, 25(5), 3468–3500. https://doi.org/10.1007/s10664-020-09843-6

Chakraborty, J., Peng, K., & Menzies, T. (2020). Making Fair ML Software using Trustworthy Explanation. 2020 35TH IEEE/ACM INTERNATIONAL CONFERENCE ON AUTOMATED SOFTWARE ENGINEERING (ASE 2020), pp. 1229–1233. https://doi.org/10.1145/3324884.3418932

Carleton, A. D., Harper, E., Menzies, T., Xie, T., Eldh, S., & Lyu, M. R. (2020). The AI Effect: Working at the Intersection of AI and SE. IEEE SOFTWARE, Vol. 37, pp. 26–35. https://doi.org/10.1109/MS.2020.2987666

Menzies, T. (2020). The Five Laws of SE for AI. IEEE SOFTWARE, Vol. 37, pp. 81–85. https://doi.org/10.1109/MS.2019.2954841

Shrikanth, N. C., & Menzies, T. (2020). What disconnects Practitioner Belief and Empirical Evidence ? 2020 ACM/IEEE 42ND INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING: COMPANION PROCEEDINGS (ICSE-COMPANION 2020), pp. 286–287. https://doi.org/10.1145/3377812.3390802

Wang, J., Yang, Y., Menzies, T., & Wang, Q. (2020). iSENSE2.0: Improving Completion-aware Crowdtesting Management with Duplicate Tagger and Sanity Checker. ACM TRANSACTIONS ON SOFTWARE ENGINEERING AND METHODOLOGY, 29(4). https://doi.org/10.1145/3394602

Menzies, T., & Shepperd, M. (2019). "Bad smells" in software analytics papers. INFORMATION AND SOFTWARE TECHNOLOGY, 112, 35–47. https://doi.org/10.1016/j.infsof.2019.04.005

Chen, J., Nair, V., Krishna, R., & Menzies, T. (2019). "Sampling" as a Baseline Optimizer for Search-Based Software Engineering. IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, 45(6), 597–614. https://doi.org/10.1109/TSE.2018.2790925

Choetkiertikul, M., Dam, H. K., Tran, T., Pham, T., Ghose, A., & Menzies, T. (2019). A Deep Learning Model for Estimating Story Points. IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, 45(7), 637–656. https://doi.org/10.1109/TSE.2018.2792473

Krishna, R., & Menzies, T. (2019). Bellwethers: A Baseline Method for Transfer Learning. IEEE TRANSACTIONS ON SOFTWARE ENGINEERING, 45(11), 1081–1105. https://doi.org/10.1109/TSE.2018.2821670

Yu, Z., & Menzies, T. (2019). FAST(2): An intelligent assistant for finding relevant papers. EXPERT SYSTEMS WITH APPLICATIONS, 120, 57–71. https://doi.org/10.1016/j.eswa.2018.11.021

Agrawal, A., Fu, W., Chen, D., Shen, X., & Menzies, T. (2019). How to "DODGE" Complex Software Analytics. IEEE Transactions on Software Engineering, 47(10), 1–1. https://doi.org/10.1109/TSE.2019.2945020

Wang, J., Li, M., Wang, S., Menzies, T., & Wang, Q. (2019). Images don't lie: Duplicate crowdtesting reports detection with screenshot information. INFORMATION AND SOFTWARE TECHNOLOGY, 110, 139–155. https://doi.org/10.1016/j.infsof.2019.03.003

Chen, J., Chakraborty, J., Clark, P., Haverlock, K., Cherian, S., & Menzies, T. (2019). Predicting Breakdowns in Cloud Services (with SPIKE). ESEC/FSE'2019: PROCEEDINGS OF THE 2019 27TH ACM JOINT MEETING ON EUROPEAN SOFTWARE ENGINEERING CONFERENCE AND SYMPOSIUM ON THE FOUNDATIONS OF SOFTWARE ENGINEERING, pp. 916–924. https://doi.org/10.1145/3338906.3340450

Yu, Z., Fahid, F., Menzies, T., Rothermel, G., Patrick, K., & Cherian, S. (2019). TERMINATOR: Better Automated UI Test Case Prioritization. ESEC/FSE'2019: PROCEEDINGS OF THE 2019 27TH ACM JOINT MEETING ON EUROPEAN SOFTWARE ENGINEERING CONFERENCE AND SYMPOSIUM ON THE FOUNDATIONS OF SOFTWARE ENGINEERING, pp. 883–894. https://doi.org/10.1145/3338906.3340448

Menzies, T. (2019). Take Control (On the Unreasonable Effectiveness of Software Analytics). 2019 IEEE/ACM 41ST INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING: SOFTWARE ENGINEERING IN PRACTICE (ICSE-SEIP 2019), pp. 265–266. https://doi.org/10.1109/ICSE-SEIP.2019.00037

Wang, J., Yang, Y., Krishna, R., Menzies, T., & Wang, Q. (2019). iSENSE: Completion-Aware Crowdtesting Management. 2019 IEEE/ACM 41ST INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING (ICSE 2019), pp. 912–923. https://doi.org/10.1109/ICSE.2019.00097

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, D., Fu, W., Krishna, R., & Menzies, T. (2018). Applications of Psychological Science for Actionable Analytics. ESEC/FSE'18: PROCEEDINGS OF THE 2018 26TH ACM JOINT MEETING ON EUROPEAN SOFTWARE ENGINEERING CONFERENCE AND SYMPOSIUM ON THE FOUNDATIONS OF SOFTWARE ENGINEERING, pp. 456–467. https://doi.org/10.1145/3236024.3236050

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., Agrawal, A., Chen, J., Fu, W., Mathew, G., Menzies, T., … Yu, Z. (2018). Data-Driven Search-based Software Engineering. 2018 IEEE/ACM 15TH INTERNATIONAL CONFERENCE ON MINING SOFTWARE REPOSITORIES (MSR), pp. 341–352. https://doi.org/10.1145/3196398.3196442

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

Yu, Z., Kraft, N. A., & Menzies, T. (2018). Finding better active learners for faster literature reviews. EMPIRICAL SOFTWARE ENGINEERING, 23(6), 3161–3186. https://doi.org/10.1007/s10664-017-9587-0

Petke, J., & Menzies, T. (2018, December). Guest Editorial for the Special Section from the 9th International Symposium on Search Based Software Engineering. INFORMATION AND SOFTWARE TECHNOLOGY, Vol. 104, pp. 194–194. https://doi.org/10.1016/j.infsof.2018.10.002

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

Agrawal, A., & Menzies, T. (2018). Is "Better Data" Better Than "Better Data Miners"? On the Benefits of Tuning SMOTE for Defect Prediction. PROCEEDINGS 2018 IEEE/ACM 40TH INTERNATIONAL CONFERENCE ON SOFTWARE ENGINEERING (ICSE), pp. 1050–1061. https://doi.org/10.1145/3180155.3180197

Hsu, C.-J., Nair, V., Menzies, T., & Freeh, V. (2018). Micky: A Cheaper Alternative for Selecting Cloud Instances. 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

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

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

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

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

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

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

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. (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., 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

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., 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

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