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References

aEHH05

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AM16

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AFC+19

Amritanshu Agrawal, Wei Fu, Di Chen, Xipeng Shen, and Tim Menzies. How to“dodge" complex software analytics. IEEE Transactions on Software Engineering (TSE), 2019.

AM18

Amritanshu Agrawal and Tim Menzies. Is Better Data Better Than Better Data Miners?: On the Benefits of Tuning SMOTE for Defect Prediction. In Proceedings of the International Conference on Software Engineering (ICSE), 1050–1061. 2018.

Aki71

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BMG11

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Bre01

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BJ95

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Cha92

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CBHK02

Nitesh V Chawla, Kevin W Bowyer, Lawrence O Hall, and W Philip Kegelmeyer. SMOTE: Synthetic Minority Over-sampling Technique. Journal of Artificial Intelligence Research, 16:321–357, 2002.

CFKM18

Di Chen, Wei Fu, Rahul Krishna, and Tim Menzies. Applications of Psychological Science for Actionable Analytics. In Proceedings of the 2018 26th ACM Joint Meeting on European Software Engineering Conference and Symposium on the Foundations of Software Engineering, 456–467. ACM, 2018.

CK94

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DAmbrosLR10

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DAmbrosLR12a

Marco D'Ambros, Michele Lanza, and Romain Robbes. Evaluating Defect Prediction Approaches : A Benchmark and an Extensive Comparison. Empirical Software Engineering, 17(4-5):531–577, 2012.

DAmbrosLR12b

Marco D'Ambros, Michele Lanza, and Romain Robbes. Evaluating Defect Prediction Approaches: A Benchmark and an Extensive Comparison. Empirical Software Engineering (EMSE), 17(4-5):531–577, 2012.

dCMS+17

Daniel Alencar da Costa, Shane McIntosh, Weiyi Shang, Uirá Kulesza, Roberta Coelho, and Ahmed E Hassan. A Framework for Evaluating the Results of the SZZ Approach for Identifying Bug-introducing Changes. IEEE Transactions on Software Engineering (TSE), 43(7):641–657, 2017.

DTG18

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EngstromRW10

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FPG03

Michael Fischer, Martin Pinzger, and Harald Gall. Populating a Release History Database from Version Control and Bug Tracking Systems. In Proceedings of the International Conference on Software Maintenance (ICSM), 23–32. 2003.

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Fox15

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FM92

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Fri01

Jerome H Friedman. Greedy Function Approximation: A Gradient Boosting Machine. Annals of statistics, pages 1189–1232, 2001.

FMS16

Wei Fu, Tim Menzies, and Xipeng Shen. Tuning for Software Analytics: Is it really necessary? Information and Software Technology, 76:135–146, 2016.

GMH15

Baljinder Ghotra, Shane McIntosh, and Ahmed E Hassan. Revisiting the Impact of Classification Techniques on the Performance of Defect Prediction Models. In Proceedings of the International Conference on Software Engineering (ICSE), 789–800. 2015.

GMH17

Baljinder Ghotra, Shane Mcintosh, and Ahmed E Hassan. A Large-scale Study of the Impact of Feature Selection Techniques on Defect Classification Models. In Proceedings of the International Conference on Mining Software Repositories (MSR), 146–157. 2017.

GB19

Alicja Gosiewska and Przemyslaw Biecek. iBreakDown: Uncertainty of Model Explanations for Non-additive Predictive Models. arXiv preprint arXiv:1903.11420, 2019.

GPD14

Georgios Gousios, Martin Pinzger, and Arie van Deursen. An Exploratory Study of the Pull-based Software Development Model. In Proceedings of the International Conference on Software Engineering (ICSE), 345–355. 2014.

GKMS00

Todd L Graves, Alan F Karr, J S Marron, and Harvey Siy. Predicting fault incidence using software change history. Transactions on Software Engineering (TSE), 26(7):653–661, 2000.

GE03

Isabelle Guyon and André Elisseeff. An Introduction to Variable and Feature Selection. Journal of Machine Learning Research, 3:1157–1182, 2003.

GyimothyFS05

Tibor Gyimóthy, Rudolf Ferenc, and Istvan Siket. Empirical validation of object-oriented metrics on open source software for fault prediction. IEEE Transactions on Software engineering, 31(10):897–910, 2005.

HBB+12

Tracy Hall, Sarah Beecham, David Bowes, David Gray, and Steve Counsell. A Systematic Literature Review on Fault Prediction Performance in Software Engineering. IEEE Transactions on Software Engineering (TSE), 38(6):1276–1304, 2012. URL: http://ieeexplore.ieee.org.pc124152.oulu.fi:8080/xpls/abs{\_}all.jsp?arnumber=6035727.

HM82

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Has09

Ahmed E Hassan. Predicting faults using the complexity of code changes. In 2009 IEEE 31st international conference on software engineering, 78–88. IEEE, 2009.

HMK12

Hideaki Hata, Osamu Mizuno, and Tohru Kikuno. Bug prediction based on fine-grained module histories. In ICSE, 200–210. 2012.

HG09

Haibo He and Edwardo A Garcia. Learning from Imbalanced Data. IEEE Transactions on Knowledge and Data Engineering (TKDE), 21(9):1263–1284, 2009.

Her14

Kim Herzig. Using pre-release test failures to build early post-release defect prediction models. In ISSRE, 300–311. 2014.

HMS05

Denis J. Hilton, John J. McClure, and Ben R. Slugoski. The psychology of counterfactual thinking. Routledge Research International Series in Social Psychology, pages 44–60, 2005.

HXL17

Qiao Huang, Xin Xia, and David Lo. Supervised vs unsupervised models: a holistic look at effort-aware just-in-time defect prediction. In Proceedings of the International Conference on Software Maintenance and Evolution (ICSME), 159–170. 2017.

JTDG20a

Jirayus Jiarpakdee, Chakkrit Tantithamthavorn, Hoa Khanh Dam, and John Grundy. An empirical study of model-agnostic techniques for defect prediction models. TSE, 2020.

JTDG20b

Jirayus Jiarpakdee, Chakkrit Tantithamthavorn, Hoa Khanh Dam, and John Grundy. An Empirical Study of Model-Agnostic Techniques for Defect Prediction Models. IEEE Transactions on Software Engineering (TSE), pages To Appear, 2020.

JTG21

Jirayus Jiarpakdee, Chakkrit Tantithamthavorn, and John Grundy. Practitioners' Perceptions of the Goals and Visual Explanations of Defect Prediction Models. In Proceedings of the International Conference on Mining Software Repositories (MSR), To Appear. 2021.

JTH19

Jirayus Jiarpakdee, Chakkrit Tantithamthavorn, and Ahmed E Hassan. The Impact of Correlated Metrics on Defect Models. IEEE Transactions on Software Engineering (TSE), pages To Appear, 2019.

JTIM16

Jirayus Jiarpakdee, Chakkrit Tantithamthavorn, Akinori Ihara, and Kenichi Matsumoto. A Study of Redundant Metrics in Defect Prediction Datasets. In Proceedings of the International Symposium on Software Reliability Engineering Workshops (ISSREW), 51–52. 2016.

JTT18a

Jirayus Jiarpakdee, Chakkrit Tantithamthavorn, and Christoph Treude. AutoSpearman: Automatically Mitigating Correlated Metrics for Interpreting Defect Models. In Proceeding of the International Conference on Software Maintenance and Evolution (ICSME), 92–103. 2018.

JTT18b

Jirayus Jiarpakdee, Chakkrit Tantithamthavorn, and Christoph Treude. AutoSpearman: Automatically Mitigating Correlated Software Metrics for Interpreting Defect Models. In Proceedings of the International Conference on Software Maintenance and Evolution (ICSME), 92–103. 2018.

JTT20a

Jirayus Jiarpakdee, Chakkrit Tantithamthavorn, and Christoph Treude. The impact of automated feature selection techniques on the interpretation of defect models. EMSE, 2020.

JTT20b

Jirayus Jiarpakdee, Chakkrit Tantithamthavorn, and Christoph Treude. The impact of automated feature selection techniques on the interpretation of defect models. Empirical Software Engineering, 25(5):3590–3638, 2020.

JKP94

George H John, Ron Kohavi, and Karl Pfleger. Irrelevant Features and the Subset Selection Problem. In Proceedings of the International Conference on Machine Learning (ICML), pages 121–129. 1994.

KMM+10

Yasutaka Kamei, Shinsuke Matsumoto, Akito Monden, Ken-ichi Matsumoto, Bram Adams, and Ahmed E Hassan. Revisiting common bug prediction findings using effort-aware models. In ICSME, 1–10. 2010.

KMM+07

Yasutaka Kamei, Akito Monden, Shinsuke Matsumoto, Takeshi Kakimoto, and Ken-ichi Matsumoto. The effects of over and under sampling on fault-prone module detection. In ESEM, 196–204. 2007.

KSA+13

Yasutaka Kamei, Emad Shihab, Bram Adams, Ahmed E. Hassan, Audris Mockus, Anand Sinha, and Naoyasu Ubayashi. A Large-Scale Empirical Study of Just-In-Time Quality Assurance. IEEE Transactions on Software Engineering (TSE), 39(6):757–773, 2013.

KGS10

T.M. Khoshgoftaar, Kehan Gao Kehan Gao, and N. Seliya. Attribute Selection and Imbalanced Data: Problems in Software Defect Prediction. Proceedings of the International Conference on Tools with Artificial Intelligence (ICTAI), 2010.

KNY+15

Mijung Kim, Jaechang Nam, Jaehyuk Yeon, Soonhwang Choi, and Sunghun Kim. REMI: Defect prediction for efficient API testing. In ESEC/FSE, 990–993. 2015.

KJ97

Ron Kohavi and George H John. Wrappers for Feature Subset Selection. Artificial Intelligence, 97(1-2):273–324, 1997.

KML+03

Helena Chmura Kraemer, George A Morgan, Nancy L Leech, Jeffrey A Gliner, Jerry J Vaske, and Robert J Harmon. Measures of Clinical Significance. Journal of the American Academy of Child & Adolescent Psychiatry (JAACAP), 42(12):1524–1529, 2003.

KM20

Rahul Krishna and Tim Menzies. From prediction to planning: improving software quality with belltree. Empirical Software Engineering, 2020.

Lea14

David B Leake. Evaluating explanations: a content theory. Psychology Press, 2014.

LBMP08

Stefan Lessmann, Bart Baesens, Christophe Mues, and Swantje Pietsch. Benchmarking Classification Models for Software Defect Prediction: A Proposed Framework and Novel Findings. IEEE Transactions on Software Engineering (TSE), 34(4):485–496, 2008.

LYAW19

Brian Y Lim, Qian Yang, Ashraf M Abdul, and Danding Wang. Why these explanations? selecting intelligibility types for explanation goals. In IUI Workshops. 2019.

Lip90

Peter Lipton. Contrastive explanation. Royal Institute of Philosophy Supplements, 27:247–266, 1990.

Lip18

Zachary C Lipton. The mythos of model interpretability: in machine learning, the concept of interpretability is both important and slippery. Queue, 16(3):31–57, 2018.

Luc0)

Lucidchart. Release management process. 2020 (accessed July 23, 2020). https://www.lucidchart.com/blog/release-management-process.

LEL18

Scott M Lundberg, Gabriel G Erion, and Su-In Lee. Consistent Individualized Feature Attribution for Tree Ensembles. arXiv preprint arXiv:1802.03888, 2018.

McC76

Thomas J McCabe. A Complexity Measure. IEEE Transactions on Software Engineering (TSE), pages 308–320, 1976.

McH13

Mary L McHugh. The Chi-square Test of Independence. Biochemia Medica, 23(2):143–149, 2013.

MK10

Thilo Mende and Rainer Koschke. Effort-aware defect prediction models. In Proceedings of the European Conference on Software Maintenance and Reengineering (CSMR), 107–116. 2010.

MGF07

Tim Menzies, Jeremy Greenwald, and Art Frank. Data Mining Static Code Attributes to Learn Defect Predictors. IEEE Transactions on Software Engineering (TSE), 33(1):2–13, 2007.

MS19

Tim Menzies and Martin Shepperd. “bad smells” in software analytics papers. IST, 112:35–47, 2019.

MZ18

Tim Menzies and Thomas Zimmermann. Software Analytics: So What? IEEE Software, pages 31–37, 2018.

Mil19

Tim Miller. Explanation in artificial intelligence: insights from the social sciences. Artificial intelligence, 267:1–38, 2019.

MH02

Audris Mockus and James D Herbsleb. Expertise browser: a quantitative approach to identifying expertise. In Proceedings of the 24th International Conference on Software Engineering. ICSE 2002, 503–512. IEEE, 2002.

NB05a

Nachiappan Nagappan and Thomas Ball. Static analysis tools as early indicators of pre-release defect density. In ICSE, 580–586. 2005.

NB05b

Nachiappan Nagappan and Thomas Ball. Use of Relative Code Churn Measures to Predict System Defect Density. Proceedings of the International Conference on Software Engineering (ICSE), pages 284–292, 2005.

NB07

Nachiappan Nagappan and Thomas Ball. Using software dependencies and churn metrics to predict field failures: an empirical case study. In First International Symposium on Empirical Software Engineering and Measurement (ESEM 2007), 364–373. IEEE, 2007.

NMB08

Nachiappan Nagappan, Brendan Murphy, and Victor Basili. The influence of organizational structure on software quality. In Proceedings of the International Conference on Software Engineering (ICSE), 521–530. 2008.

NZZ+10

Nachiappan Nagappan, Andreas Zeller, Thomas Zimmermann, Kim Herzig, and Brendan Murphy. Change Bursts as Defect Predictors. In Proceedings of the International Symposium on Software Reliability Engineering (ISSRE), 309–318. 2010.

Nam14

Jaechang Nam. Survey on software defect prediction. Department of Compter Science and Engineerning, The Hong Kong University of Science and Technology, Tech. Rep, 2014.

PO11

Chris Parnin and Alessandro Orso. Are automated debugging techniques actually helping programmers? In ISSTA, 199–209. 2011.

PPB19

Luca Pascarella, Fabio Palomba, and Alberto Bacchelli. Fine-grained just-in-time defect prediction. JSS, 150:22–36, 2019.

PD07

Lourdes Pelayo and Scott Dick. Applying novel resampling strategies to software defect prediction. Annual Conference of the North American Fuzzy Information Processing Society - NAFIPS, pages 69–72, 2007.

PM20

Kewen Peng and Tim Menzies. Defect Reduction Planning (using TimeLIME). arXiv preprint arXiv:2006.07416, 2020.

PTJ+21

Chanathip Pornprasit, Chakkrit Tantithamthavorn, Jirayus Jiarpakdee, Michael Fu, and Patanamon Thongtanunam. PyExplainer: Explaining the Predictions of Just-In-Time Defect Models. In Proceedings of the International Conference on Automated Software Engineering (ASE), To Appear. 2021.

RD13

Foyzur Rahman and Premkumar Devanbu. How, and Why, Process Metrics are Better. In Proceedings of the International Conference on Software Engineering (ICSE), 432–441. 2013.

RKBD14

Foyzur Rahman, Sameer Khatri, Earl T Barr, and Premkumar Devanbu. Comparing static bug finders and statistical prediction. In ICSE, 424–434. 2014.

RR15

Md Tajmilur Rahman and Peter C Rigby. Release stabilization on linux and chrome. IEEE Software, 32(2):81–88, 2015.

RTJ+21

Dilini Rajapaksha, Chakkrit Tantithamthavorn, Jirayus Jiarpakdee, Christoph Bergmeir, John Grundy, and Wray Buntine. SQAPlanner: Generating Data-Informed Software Quality Improvement Plans. IEEE Transactions on Software Engineering, 2021.

RHG+16

Baishakhi Ray, Vincent Hellendoorn, Saheel Godhane, Zhaopeng Tu, Alberto Bacchelli, and Premkumar Devanbu. On the Naturalness of Buggy Code. In Proceedings of the International Conference on Software Engineering (ICSE), 428–439. 2016.

RSG16a

Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. Model-agnostic Interpretability of Machine Learning. arXiv preprint arXiv:1606.05386, 2016.

RSG16b

Marco Tulio Ribeiro, Sameer Singh, and Carlos Guestrin. Why should I trust you?: Explaining the Predictions of Any Classifier. In Proceedings of the International Conference on Knowledge Discovery and Data Mining (KDDM), 1135–1144. 2016.

Sal84

Wesley C. Salmon. Scientific explanation and the causal structure of the world. Princeton University Press Princeton, N.J, 1984.

SKVanHulseF14

Chris Seiffert, Taghi M. Khoshgoftaar, Jason Van Hulse, and Andres Folleco. An empirical study of the classification performance of learners on imbalanced and noisy software quality data. Information Sciences, pages 571–595, 2014.

Shi12

Emad Shihab. An Exploration of Challenges Limiting Pragmatic Software Defect Prediction. PhD thesis, Queen's University, 2012.

SNAH14

Mark D Syer, Meiyappan Nagappan, Bram Adams, and Ahmed E Hassan. Replicating and re-evaluating the theory of relative defect-proneness. IEEE Transactions on Software Engineering, 41(2):176–197, 2014.

TTDM15

Ming Tan, Lin Tan, Sashank Dara, and Caleb Mayeux. Online defect prediction for imbalanced data. In Proceedings of the International Conference on Software Engineering (ICSE), volume 2, 99–108. 2015.

Tan16

Chakkrit Tantithamthavorn. Towards a Better Understanding of the Impact of Experimental Components on Defect Prediction Modelling. In Companion Proceeding of the International Conference on Software Engineering (ICSE), 867––870. 2016.

THM19

Chakkrit Tantithamthavorn, Ahmed E Hassan, and Kenichi Matsumoto. The Impact of Class Rebalancing Techniques on The Performance and Interpretation of Defect Prediction Models. IEEE Transactions on Software Engineering (TSE), pages To Appear, 2019.

TH18

Chakkrit Tantithamthavorn and Ahmed E. Hassan. An Experience Report on Defect Modelling in Practice: Pitfalls and Challenges. In In Proceedings of the International Conference on Software Engineering: Software Engineering in Practice Track (ICSE-SEIP), 286–295. 2018.

TMHM16a

Chakkrit Tantithamthavorn, Shane McIntosh, Ahmed E Hassan, and Kenichi Matsumoto. Automated Parameter Optimization of Classification Techniques for Defect Prediction Models. In Proceedings of the International Conference on Software Engineering (ICSE), 321–332. 2016.

TMHM16b

Chakkrit Tantithamthavorn, Shane McIntosh, Ahmed E Hassan, and Kenichi Matsumoto. Comments on \textquotedblleft Researcher Bias: The Use of Machine Learning in Software Defect Prediction\textquotedblright . IEEE Transactions on Software Engineering (TSE), 42(11):1092–1094, 2016.

TMHM17

Chakkrit Tantithamthavorn, Shane McIntosh, Ahmed E Hassan, and Kenichi Matsumoto. An Empirical Comparison of Model Validation Techniques for Defect Prediction Models. IEEE Transactions on Software Engineering (TSE), 43(1):1–18, 2017.

TMH+15

Chakkrit Tantithamthavorn, Shane McIntosh, Ahmed E. Hassan, Akinori Ihara, and Kenichi Matsumoto. The Impact of Mislabelling on the Performance and Interpretation of Defect Prediction Models. In Proceeding of the International Conference on Software Engineering (ICSE), 812–823. 2015.

TMHM18

Chakkrit Tantithamthavorn, Shane McIntosh, Ahmed E. Hassan, and Kenichi Matsumoto. The Impact of Automated Parameter Optimization on Defect Prediction Models. IEEE Transactions on Software Engineering (TSE), pages 683–711, 2018.

TMHI16

Patanamon Thongtanunam, Shane McIntosh, Ahmed E Hassan, and Hajimu Iida. Revisiting Code Ownership and its Relationship with Software Quality in the Scope of Modern Code Review. In Proceedings of the International Conference on Software Engineering (ICSE), 1039–1050. 2016.

TMHI15

Patanamon Thongtanunam, Shane McIntosh, Ahmed E. Hassan, and Hajimu Iida. Investigating code review practices in defective files: an empirical study of the qt system. In MSR, 168–179. 2015.

VBW02

Jeroen Van Bouwel and Erik Weber. Remote causes, bad explanations? Journal for the Theory of Social Behaviour, 32(4):437–449, 2002.

WXH+18

Zhiyuan Wan, Xin Xia, Ahmed E Hassan, David Lo, Jianwei Yin, and Xiaohu Yang. Perceptions, expectations, and challenges in defect prediction. IEEE Transactions on Software Engineering, 2018.

WY13

Shuo Wang and Xin Yao. Using Class Imbalance Learning for Software Defect Prediction. IEEE Transactions on Reliability, 62(2):434–443, 2013.

WLNT18

Song Wang, Taiyue Liu, Jaechang Nam, and Lin Tan. Deep Semantic Feature Learning for Software Defect Prediction. IEEE Transactions on Software Engineering (TSE), 2018.

WLT16

Song Wang, Taiyue Liu, and Lin Tan. Automatically Learning Semantic Features for Defect Prediction. In Proceedings of the International Conference on Software Engineering (ICSE), 297–308. 2016.

WTT+20

Supatsara Wattanakriengkrai, Patanamon Thongtanunam, Chakkrit Tantithamthavorn, Hideaki Hata, and Kenichi Matsumoto. Predicting defective lines using a model-agnostic technique. IEEE Transactions on Software Engineering (TSE), 2020.

XLS+15

Xin Xia, David Lo, Emad Shihab, Xinyu Wang, and Xiaohu Yang. Elblocker: Predicting blocking bugs with ensemble imbalance learning. Information and Software Technology, 61:93–106, 2015.

YLX+17

Xin-Li Yang, David Lo, Xin Xia, Qiao Huang, and Jian-Ling Sun. High-impact bug report identification with imbalanced learning strategies. Journal of Computer Science and Technology, 2017.

YLH+16

Xinli Yang, David Lo, Qiao Huang, Xin Xia, and Jianling Sun. Automated identification of high impact bug reports leveraging imbalanced learning strategies. Computer Software and Applications Conference (COMPSAC), 1:227–232, 2016.

ZN08

T. Zimmermann and N. Nagappan. Predicting defects using network analysis on dependency graphs. In Proceedings of the International Conference on Software Engineering (ICSE'08), 531–540. 2008.

ZNG+09

Thomas Zimmermann, Nachiappan Nagappan, Harald Gall, Emanuel Giger, and Brendan Murphy. Cross-project Defect Prediction. In Proceedings of the European Software Engineering Conference and the Symposium on the Foundations of Software Engineering (ESEC/FSE), 91–100. 2009.

ZPZ07

Thomas Zimmermann, Rahul Premraj, and Andreas Zeller. Predicting Defects for Eclipse. In Proceedings of the International Workshop on Predictor Models in Software Engineering (PROMISE), 9–19. 2007.

SliwerskiZZ05

Jacek Śliwerski, Thomas Zimmermann, and Andreas Zeller. When do changes induce fixes? ACM sigsoft software engineering notes, 30(4):1–5, 2005.

KameiMondenMatsumoto+07

Y. Kamei, A. Monden, S. Matsumoto, T. Kakimoto, and K. Matsumoto. The effects of over and under sampling on fault-prone module detection. In First International Symposium on Empirical Software Engineering and Measurement (ESEM 2007), 196–204. 2007.