The ‘as code’ activities: development anti-patterns for infrastructure as code
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[1] Laurie A. Williams,et al. Characterizing Defective Configuration Scripts Used for Continuous Deployment , 2018, 2018 IEEE 11th International Conference on Software Testing, Verification and Validation (ICST).
[2] James Turnbull. Pulling Strings with Puppet: Automated System Administration Done Right , 2008 .
[3] ShenXipeng,et al. Tuning for software analytics , 2016 .
[4] Frank Elberzhager,et al. Guiding Testing Activities by Predicting Defect-Prone Parts Using Product and Inspection Metrics , 2012, 2012 38th Euromicro Conference on Software Engineering and Advanced Applications.
[5] Tracy Hall,et al. A Systematic Literature Review on Fault Prediction Performance in Software Engineering , 2012, IEEE Transactions on Software Engineering.
[6] Laurie A. Williams,et al. Source Code Properties of Defective Infrastructure as Code Scripts , 2018, Inf. Softw. Technol..
[7] Jacob Cohen. A Coefficient of Agreement for Nominal Scales , 1960 .
[8] Thomas J. Mowbray,et al. AntiPatterns: Refactoring Software, Architectures, and Projects in Crisis , 1998 .
[9] Vanda Broughton,et al. Sage Dictionary of Statistics: A Practical Resource for Students in the Social Sciences , 2005 .
[10] Kief Morris,et al. Infrastructure as Code: Managing Servers in the Cloud , 2016 .
[11] Diomidis Spinellis,et al. Does Your Configuration Code Smell? , 2016, 2016 IEEE/ACM 13th Working Conference on Mining Software Repositories (MSR).
[12] Tim Menzies,et al. Data Mining Static Code Attributes to Learn Defect Predictors , 2007, IEEE Transactions on Software Engineering.
[13] David A. Freedman,et al. Statistical Models: Theory and Practice: References , 2005 .
[14] Georgios Gousios,et al. How good is your puppet? An empirically defined and validated quality model for puppet , 2018, SANER.
[15] Gerald M. Weinberg,et al. Quality Software Management Volume 1: Systems Thinking , 1991 .
[16] Zachary Munn,et al. Systematic review or scoping review? Guidance for authors when choosing between a systematic or scoping review approach , 2018, BMC Medical Research Methodology.
[17] Foutse Khomh,et al. Code Authorship and Fault-proneness of Open-Source Android Applications: An Empirical Study , 2017, PROMISE.
[18] Chris Parnin,et al. Gang of Eight: A Defect Taxonomy for Infrastructure as Code Scripts , 2020, 2020 IEEE/ACM 42nd International Conference on Software Engineering (ICSE).
[19] Daniela E. Damian,et al. Selecting Empirical Methods for Software Engineering Research , 2008, Guide to Advanced Empirical Software Engineering.
[20] Leo Breiman,et al. Random Forests , 2001, Machine Learning.
[21] Wei-Yin Loh,et al. Classification and regression trees , 2011, WIREs Data Mining Knowl. Discov..
[22] Gail M. Sullivan,et al. Using Effect Size-or Why the P Value Is Not Enough. , 2012, Journal of graduate medical education.
[23] Laurie A. Williams,et al. Validating software metrics: A spectrum of philosophies , 2012, TSEM.
[24] Laurie A. Williams,et al. What Questions Do Programmers Ask about Configuration as Code? , 2018, 2018 IEEE/ACM 4th International Workshop on Rapid Continuous Software Engineering (RCoSE).
[25] Vipin Kumar,et al. Introduction to Data Mining , 2022, Data Mining and Machine Learning Applications.
[26] Carl J. Huberty,et al. Applied MANOVA and discriminant analysis , 2006 .
[27] Shane McIntosh,et al. Modern Release Engineering in a Nutshell -- Why Researchers Should Care , 2016, 2016 IEEE 23rd International Conference on Software Analysis, Evolution, and Reengineering (SANER).
[28] Zhen Ming Jiang,et al. Characterizing and Detecting Anti-Patterns in the Logging Code , 2017, 2017 IEEE/ACM 39th International Conference on Software Engineering (ICSE).
[29] Bente Anda,et al. Experiences from conducting semi-structured interviews in empirical software engineering research , 2005, 11th IEEE International Software Metrics Symposium (METRICS'05).
[30] Meiyappan Nagappan,et al. Curating GitHub for engineered software projects , 2017, Empirical Software Engineering.
[31] Ayse Basar Bener,et al. Data mining source code for locating software bugs: A case study in telecommunication industry , 2009, Expert Syst. Appl..
[32] Brendan Murphy,et al. Can developer-module networks predict failures? , 2008, SIGSOFT '08/FSE-16.
[33] H. Arksey,et al. Scoping studies: towards a methodological framework , 2005 .
[34] Yuriy Brun,et al. Tortoise: Interactive system configuration repair , 2017, 2017 32nd IEEE/ACM International Conference on Automated Software Engineering (ASE).
[35] Jonathan I. Maletic,et al. What's a Typical Commit? A Characterization of Open Source Software Repositories , 2008, 2008 16th IEEE International Conference on Program Comprehension.
[36] Lionel C. Briand,et al. A systematic and comprehensive investigation of methods to build and evaluate fault prediction models , 2010, J. Syst. Softw..
[37] Shane McIntosh,et al. Automated Parameter Optimization of Classification Techniques for Defect Prediction Models , 2016, 2016 IEEE/ACM 38th International Conference on Software Engineering (ICSE).
[38] Christian Bird,et al. Code Reviewing in the Trenches: Challenges and Best Practices , 2018, IEEE Software.
[39] Ayse Basar Bener,et al. Practical considerations in deploying statistical methods for defect prediction: A case study within the Turkish telecommunications industry , 2010, Inf. Softw. Technol..
[40] Shari Lawrence Pfleeger,et al. Personal Opinion Surveys , 2008, Guide to Advanced Empirical Software Engineering.
[41] Arjun Guha,et al. Rehearsal: a configuration verification tool for puppet , 2015, PLDI.
[42] Harald C. Gall,et al. Don't touch my code!: examining the effects of ownership on software quality , 2011, ESEC/FSE '11.
[43] Premkumar T. Devanbu,et al. How, and why, process metrics are better , 2013, 2013 35th International Conference on Software Engineering (ICSE).
[44] Paul Hudak,et al. Modular domain specific languages and tools , 1998, Proceedings. Fifth International Conference on Software Reuse (Cat. No.98TB100203).
[45] Ahmed E. Hassan,et al. Predicting faults using the complexity of code changes , 2009, 2009 IEEE 31st International Conference on Software Engineering.
[46] Emerson R. Murphy-Hill,et al. Improving developer participation rates in surveys , 2013, 2013 6th International Workshop on Cooperative and Human Aspects of Software Engineering (CHASE).
[47] Tim Menzies,et al. Tuning for Software Analytics: is it Really Necessary? , 2016, Inf. Softw. Technol..
[48] Johnny Saldaña,et al. The Coding Manual for Qualitative Researchers , 2009 .
[49] Premkumar T. Devanbu,et al. Belief & Evidence in Empirical Software Engineering , 2016, 2016 IEEE/ACM 38th International Conference on Software Engineering (ICSE).
[50] Chris Parnin,et al. The Seven Sins: Security Smells in Infrastructure as Code Scripts , 2019, 2019 IEEE/ACM 41st International Conference on Software Engineering (ICSE).
[51] Jez Humble,et al. Continuous Delivery: Reliable Software Releases Through Build, Test, and Deployment Automation , 2010 .
[52] TurhanBurak,et al. Data mining source code for locating software bugs , 2009 .
[53] Elliot Soloway,et al. Where the bugs are , 1985, CHI '85.
[54] Gabriele Bavota,et al. An empirical study on developer‐related factors characterizing fix‐inducing commits , 2017, J. Softw. Evol. Process..
[55] Laurie A. Williams,et al. Secure open source collaboration: an empirical study of linus' law , 2009, CCS.
[56] Tim Menzies,et al. Assessing Developer Beliefs: A Reply to "Perceptions, Expectations, and Challenges in Defect Prediction" , 2019, ArXiv.
[57] Leo Breiman,et al. Classification and Regression Trees , 1984 .
[58] Eelco Visser,et al. DSL Engineering - Designing, Implementing and Using Domain-Specific Languages , 2013 .
[59] Vahid Garousi,et al. Smells in software test code: A survey of knowledge in industry and academia , 2018, J. Syst. Softw..
[60] Eric Van Wyk,et al. Attribute Grammar-Based Language Extensions for Java , 2007, ECOOP.
[61] Jr. Frederick P. Brooks,et al. The mythical man-month (anniversary ed.) , 1995 .
[62] Shane McIntosh,et al. An Empirical Comparison of Model Validation Techniques for Defect Prediction Models , 2017, IEEE Transactions on Software Engineering.
[63] J. R. Landis,et al. The measurement of observer agreement for categorical data. , 1977, Biometrics.
[64] Stephen Peckham,et al. Asking the right questions: Scoping studies in the commissioning of research on the organisation and delivery of health services , 2008, Health research policy and systems.
[65] Bram Adams,et al. Co-evolution of Infrastructure and Source Code - An Empirical Study , 2015, 2015 IEEE/ACM 12th Working Conference on Mining Software Repositories.
[66] N. Cliff. Dominance statistics: Ordinal analyses to answer ordinal questions. , 1993 .
[67] Ahmed E. Hassan,et al. Prioritizing the creation of unit tests in legacy software systems , 2011, Softw. Pract. Exp..
[68] H. B. Mann,et al. On a Test of Whether one of Two Random Variables is Stochastically Larger than the Other , 1947 .
[69] Shane McIntosh,et al. Revisiting the Impact of Classification Techniques on the Performance of Defect Prediction Models , 2015, 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering.
[70] Robert C. Martin. The Clean Coder: A Code of Conduct for Professional Programmers , 2011 .