Omniscient DevOps Analytics

DevOps predicates the continuity between Development and Operations teams at an unprecedented scale. Also, the continuity does not stop at tools, or processes but goes beyond into organizational practices, collaboration, co-located and coordinated effort. We conjecture that this unprecedented scale of continuity requires predictive analytics which are omniscient, that is (i) transversal to the technical, organizational, and social stratification in software processes and (ii) correlate all strata to provide a live and holistic snapshot of software development, its operations, and organization. Elaborating this conjecture, we illustrate a set of metrics to be used in the DevOps scenario and overview challenges and future research directions.

[1]  Raffaela Mirandola,et al.  DevOps Service Observability By-Design: Experimenting with Model-View-Controller , 2018, ESOCC.

[2]  Robert L. Nord,et al.  Technical debt in software development: from metaphor to theory report on the third international workshop on managing technical debt , 2012, SOEN.

[3]  Harald C. Gall,et al.  Context is king: The developer perspective on the usage of static analysis tools , 2018, 2018 IEEE 25th International Conference on Software Analysis, Evolution and Reengineering (SANER).

[4]  Martin P. Robillard,et al.  Revisiting Turnover-Induced Knowledge Loss in Software Projects , 2017, 2017 IEEE International Conference on Software Maintenance and Evolution (ICSME).

[5]  J. Coleman Foundations of Social Theory , 1990 .

[6]  Marco Tulio Valente,et al.  A Comparison of Three Algorithms for Computing Truck Factors , 2017, 2017 IEEE/ACM 25th International Conference on Program Comprehension (ICPC).

[7]  Yann-Gaël Guéhéneuc,et al.  DECOR: A Method for the Specification and Detection of Code and Design Smells , 2010, IEEE Transactions on Software Engineering.

[8]  Yuming Zhou,et al.  Examining the Potentially Confounding Effect of Class Size on the Associations between Object-Oriented Metrics and Change-Proneness , 2009, IEEE Transactions on Software Engineering.

[9]  Andy Zaidman,et al.  Does Refactoring of Test Smells Induce Fixing Flaky Tests? , 2017, 2017 IEEE International Conference on Software Maintenance and Evolution (ICSME).

[10]  Ahmed E. Hassan,et al.  Predicting faults using the complexity of code changes , 2009, 2009 IEEE 31st International Conference on Software Engineering.

[11]  Andrea De Lucia,et al.  Detecting code smells using machine learning techniques: Are we there yet? , 2018, 2018 IEEE 25th International Conference on Software Analysis, Evolution and Reengineering (SANER).

[12]  Andy Zaidman,et al.  On the Relation of Test Smells to Software Code Quality , 2018, 2018 IEEE International Conference on Software Maintenance and Evolution (ICSME).

[13]  Gabriele Bavota,et al.  Mining Version Histories for Detecting Code Smells , 2015, IEEE Transactions on Software Engineering.

[14]  I. G. MacDonald,et al.  Symmetric functions and Hall polynomials , 1979 .

[15]  Gabriele Bavota,et al.  A Developer Centered Bug Prediction Model , 2018, IEEE Transactions on Software Engineering.

[16]  Liming Zhu,et al.  DevOps - A Software Architect's Perspective , 2015, SEI series in software engineering.

[17]  Francesca Arcelli Fontana,et al.  Toward a Smell-Aware Bug Prediction Model , 2019, IEEE Transactions on Software Engineering.

[18]  Tim Menzies,et al.  Actionable Analytics for Software Engineering , 2018, IEEE Softw..

[19]  Andrea De Lucia,et al.  Automatic Test Smell Detection Using Information Retrieval Techniques , 2018, 2018 IEEE International Conference on Software Maintenance and Evolution (ICSME).

[20]  Gabriele Bavota,et al.  On the diffuseness and the impact on maintainability of code smells: a large scale empirical investigation , 2018, Empirical Software Engineering.

[21]  B. Kitchenham,et al.  Case Studies for Method and Tool Evaluation , 1995, IEEE Softw..

[22]  Sam Han,et al.  Theorizing New Media: Reflexivity, Knowledge, and the Web 2.0* , 2010 .

[23]  Mary E. Helander,et al.  Using Software Repositories to Investigate Socio-technical Congruence in Development Projects , 2007, Fourth International Workshop on Mining Software Repositories (MSR'07:ICSE Workshops 2007).

[24]  Meir M. Lehman,et al.  Laws of Software Evolution Revisited , 1996, EWSPT.

[25]  Andrea De Lucia,et al.  [Journal First] The Scent of a Smell: An Extensive Comparison Between Textual and Structural Smells , 2018, 2018 IEEE/ACM 40th International Conference on Software Engineering (ICSE).

[26]  Premkumar T. Devanbu,et al.  Gender and Tenure Diversity in GitHub Teams , 2015, CHI.

[27]  M. E. Conway HOW DO COMMITTEES INVENT , 1967 .

[28]  Gregorio Robles,et al.  Developer Turnover in Global, Industrial Open Source Projects: Insights from Applying Survival Analysis , 2017, 2017 IEEE 12th International Conference on Global Software Engineering (ICGSE).

[29]  Krishna P. Gummadi,et al.  Measurement and analysis of online social networks , 2007, IMC '07.

[30]  Alexander Chatzigeorgiou,et al.  Identification of Move Method Refactoring Opportunities , 2009, IEEE Transactions on Software Engineering.

[31]  T. Mens,et al.  Socio-technical evolution of the Ruby ecosystem in GitHub , 2017, 2017 IEEE 24th International Conference on Software Analysis, Evolution and Reengineering (SANER).

[32]  Philippe Kruchten,et al.  Social debt in software engineering: insights from industry , 2015, Journal of Internet Services and Applications.

[33]  Andrea De Lucia,et al.  Hypervolume-Based Search for Test Case Prioritization , 2015, SSBSE.

[34]  Andrea De Lucia,et al.  An Exploratory Study on the Relationship between Changes and Refactoring , 2017, 2017 IEEE/ACM 25th International Conference on Program Comprehension (ICPC).

[35]  Patti M. Valkenburg,et al.  The Social Media Disorder Scale , 2016, Comput. Hum. Behav..

[36]  Andrea De Lucia,et al.  Dynamic Selection of Classifiers in Bug Prediction: An Adaptive Method , 2017, IEEE Transactions on Emerging Topics in Computational Intelligence.

[37]  Andrea De Lucia,et al.  Enhancing change prediction models using developer-related factors , 2018, J. Syst. Softw..

[38]  Sven Apel,et al.  From Developer Networks to Verified Communities: A Fine-Grained Approach , 2015, 2015 IEEE/ACM 37th IEEE International Conference on Software Engineering.

[39]  Lisa Crispin,et al.  Driving Software Quality: How Test-Driven Development Impacts Software Quality , 2006, IEEE Software.

[40]  Gabriele Bavota,et al.  An empirical investigation into the nature of test smells , 2016, 2016 31st IEEE/ACM International Conference on Automated Software Engineering (ASE).

[41]  Elaine J. Weyuker,et al.  Predicting the location and number of faults in large software systems , 2005, IEEE Transactions on Software Engineering.

[42]  Jeff Vass,et al.  Revisiting the Three Rs of Social Machines: Reflexivity, Recognition and Responsivity , 2015, WWW.

[43]  Patricia Lago,et al.  Organizational social structures for software engineering , 2013, CSUR.

[44]  Marco Tulio Valente,et al.  A novel approach for estimating Truck Factors , 2016, 2016 IEEE 24th International Conference on Program Comprehension (ICPC).

[45]  Audris Mockus,et al.  Quantifying and Mitigating Turnover-Induced Knowledge Loss: Case Studies of Chrome and a Project at Avaya , 2016, 2016 IEEE/ACM 38th International Conference on Software Engineering (ICSE).

[46]  Alexander Serebrenik,et al.  Poster: How Do Community Smells Influence Code Smells? , 2018, 2018 IEEE/ACM 40th International Conference on Software Engineering: Companion (ICSE-Companion).

[47]  Gabriele Bavota,et al.  An experimental investigation on the innate relationship between quality and refactoring , 2015, J. Syst. Softw..

[48]  Andrea De Lucia,et al.  A textual-based technique for Smell Detection , 2016, 2016 IEEE 24th International Conference on Program Comprehension (ICPC).

[49]  Gabriele Bavota,et al.  Do They Really Smell Bad? A Study on Developers' Perception of Bad Code Smells , 2014, 2014 IEEE International Conference on Software Maintenance and Evolution.

[50]  Gabriele Bavota,et al.  A large-scale empirical study on the lifecycle of code smell co-occurrences , 2018, Inf. Softw. Technol..

[51]  Sebastian G. Elbaum,et al.  Code churn: a measure for estimating the impact of code change , 1998, Proceedings. International Conference on Software Maintenance (Cat. No. 98CB36272).