This keynote discusses the use of Predictive Analytics for Software Engineering, and in particular for Software Defect Prediction and Software Testing, by presenting the latest results achieved in these fields leveraging Artificial Intelligence, Search-based and Machine Learning methods, and by giving some directions for future work

Sarro, F

UCL Discovery

Predictive Analytics for Software TestingKeynote Paper∗Federica SarroDepartment of Computer Science, University College LondonLondon, United Kingdomf.sarro@ucl.ac.ukABSTRACTThis keynote discusses the use of Predictive Analytics for SoftwareEngineering, and in particular for Software Defect Prediction andSoftware Testing, by presenting the latest results achieved in thesefields leveraging Artificial Intelligence, Search-based and MachineLearning methods, and by giving some directions for future work.CCS CONCEPTS• Software and its engineering→ Search-based software en-gineering;KEYWORDSPredictive Analytics, Search-based Predictive ModellingACM Reference Format:Federica Sarro. 2018. Predictive Analytics for Software Testing: KeynotePaper. In SBST’18: IEEE/ACM 11th International Workshop on Search-BasedSoftware Testing , May 28–29, 2018, Gothenburg, Sweden. ACM, New York,NY, USA, 1 page. https://doi.org/10.1145/3194718.31947301 INTRODUCTIONThe ever increasing volume of data produced by realising and usingsoftware calls for a new generation of analytical techniques that canhelp software engineers better understand their software processes,products and customers. Devising effective Predictive Analyticsfor Software Engineering holds the promise to maximise productquality, users’ satisfaction and revenues.This keynote talk illustrates how search-based heuristics can beused to build effective predictive models for software engineering[2, 7] and highlights some of the recent developments in the pro-duction of adaptive [6, 11, 12], human-competitive [13] and scalablesolutions [3, 4, 10].The keynote also introduces the mutually beneficial relationshipbetween predictive modelling and software testing by illustratingnew ways to strengthen current testing practise through the use ofpredictive models [14] and, viceversa, to construct better predictivemodels through the use of information available from softwaretesting activities [1].∗This one page paper provides an outline of the keynote talk given by Federica Sarroat SBST’18, with pointers to the literature for details of the results covered.Permission to make digital or hard copies of part or all of this work for personal orclassroom use is granted without fee provided that copies are not made or distributedfor profit or commercial advantage and that copies bear this notice and the full citationon the first page. Copyrights for third-party components of this work must be honored.For all other uses, contact the owner/author(s).SBST’18, May 28–29, 2018, Gothenburg, Sweden© 2018 Copyright held by the owner/author(s).ACM ISBN 978-1-4503-5741-8/18/05.https://doi.org/10.1145/3194718.3194730The talk concludes discussing how text mining, analytics andsearch-based techniques can be combined to obtain more efficienttest automation, for example, exploiting artefacts written in naturallanguage to infer behavioural models of software applications andto discover issues earlier in the process [5, 8, 9, 15].ACKNOWLEDGMENTSThis work is supported by the EPSRC Programme Grant EP/J017515.REFERENCES[1] D. Bowes, T. Hall, M. Harman, Y. Jia, F. Sarro, and F. Wu. 2016. Mutation-awareFault Prediction. In Procs. of the 25th International Symposium on Software Testingand Analysis (ISSTA’16). ACM, 330–341. https://doi.org/10.1145/2931037.2931039[2] F. Ferrucci, M. Harman, and F. Sarro. 2014. Search-Based Software Project Man-agement. In Software Project Management in a ChangingWorld. Springer, 373–399.[3] F. Ferrucci, P. Salza, and F. Sarro. 2017. Using Hadoop MapReduce for Paral-lel Genetic Algorithms: A Comparison of the Global, Grid and Island Models.Evolutionary Computation (2017), 1–33. https://doi.org/10.1162/evco_a_00213[4] L. Di Geronimo, F. Ferrucci, A. Murolo, and F. Sarro. 2012. A Parallel Genetic Al-gorithm Based on HadoopMapReduce for the Automatic Generation of JUnit TestSuites. In Procs. of the 5th International Conference on Software Testing, Verificationand Validation (ICST’12). 785–793. https://doi.org/10.1109/ICST.2012.177[5] M. Harman, A. Al-Subaihin, Y. Jia, W. Martin, F. Sarro, and Y. Zhang. 2016. MobileApp and App Store Analysis, Testing and Optimisation. In Procs. of the Interna-tional Conference on Mobile Software Engineering and Systems (MOBILESoft’16).ACM, 243–244. https://doi.org/10.1145/2897073.2897076[6] M. Harman, S. Islam, Y. Jia, L. L. Minku, F. Sarro, and K. Srivisut. 2014. Less isMore: Temporal Fault Predictive Performance over Multiple Hadoop Releases.In Procs. of the International Symposium on Search-Based Software Engineering(SSBSE’14). Springer, 240–246. https://doi.org/10.1007/978-3-319-09940-8_19[7] Ruchika M., Megha K., and Rajeev R. R. 2017. On the application of search-basedtechniques for software engineering predictive modeling: A systematic reviewand future directions. Swarm and Evolutionary Computation 32 (2017), 85 – 109.[8] W. Martin, F. Sarro, and M. Harman. 2016. Causal Impact Analysis for AppReleases in Google Play. In Procs. of the 24th ACM SIGSOFT InternationalSymposium on Foundations of Software Engineering (FSE 2016). ACM, 435–446.https://doi.org/10.1145/2950290.2950320[9] W. Martin, F. Sarro, Y. Jia, Y. Zhang, and M. Harman. 2017. A Survey of AppStore Analysis for Software Engineering. IEEE Trans. Software Engineering 43, 9(2017), 817–847. https://doi.org/10.1109/TSE.2016.2630689[10] S. Di Martino, F. Ferrucci, V. Maggio, and F. Sarro. 2013. Towards MigratingGenetic Algorithms for Test Data Generation to the Cloud. (2013), 113–135.https://doi.org/10.4018/978-1-4666-2536-5.ch006[11] F. Sarro, S. Di Martino, F. Ferrucci, and C. Gravino. 2012. A Further Analysis onthe Use of Genetic Algorithm to Configure Support Vector Machines for Inter-release Fault Prediction. In Procs. of the 27th Annual ACM Symposium on AppliedComputing (SAC’12). ACM, 1215–1220. https://doi.org/10.1145/2245276.2231967[12] F. Sarro, F. Ferrucci, M. Harman, A. Manna, and J. Ren. 2017. Adaptive Multi-Objective Evolutionary Algorithms for Overtime Planning in Software Projects.IEEE Trans. Software Eng. 43, 10 (2017), 898–917. https://doi.org/10.1109/TSE.2017.2650914[13] F. Sarro, A. Petrozziello, and M. Harman. 2016. Multi-objective Software EffortEstimation. In Procs.of the 38th International Conference on Software Engineering(ICSE’16). ACM, 619–630. https://doi.org/10.1145/2884781.2884830[14] J. Zhang, Z. Wang, L. Zhang, D. Hao, L. Zang, S. Cheng, and L. Zhang. 2016.Predictive Mutation Testing. In Procs. of the 25th International Symposium onSoftware Testing and Analysis (ISSTA’16). ACM, 342–353.[15] Y. Zhang, M. Harman, Y. Jia, and F. Sarro. 2015. Inferring test models from Kate’sbug reports using multi-objective search. In Procs. of the International Symposiumon Search Based Software Engineering (SSBSE’15). Springer, 301–307.

Predictive analytics for software testing: Keynote paper

http://discovery.ucl.ac.uk/10054557/1/header.pdf

Predictive analytics for software testing: Keynote paper

Abstract

Similar works

Full text

Available Versions

UCL Discovery