{"title":"Industry-Academia Collaboration in Software Testing: An Overview of TAIC PART 2017","authors":"Takashi Kitamura, Emil Alégroth, R. Ramler","doi":"10.1109/ICSTW.2016.36","DOIUrl":"https://doi.org/10.1109/ICSTW.2016.36","url":null,"abstract":"Collaboration between industry and academia in software testing leads to improvement and innovation in industry, and it is the basis for achieving transferable and empirically evaluated results. Thus, the aim of TAIC PART is to forge collaboration between industry and academia on the challenging and exciting problem of real-world software testing. The workshop is promoted by representatives of both industry and academia, bringing together industrial software engineers and testers with researchers working on theory and practice of software testing. We present an overview of the 12th Workshop on Testing: Academia-Industry Collaboration, Practice and Research Techniques (TAIC PART 2017) and its contributions.","PeriodicalId":312017,"journal":{"name":"2017 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW)","volume":"277 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2016-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134215200","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Combinatorial Interaction Testing for Automated Constraint Repair","authors":"A. Gargantini, J. Petke, M. Radavelli","doi":"10.1109/ICSTW.2017.44","DOIUrl":"https://doi.org/10.1109/ICSTW.2017.44","url":null,"abstract":"Highly-configurable software systems can be easily adapted to address user's needs. Modelling parameter configurations and their relationships can facilitate software reuse. Combinatorial Interaction Testing (CIT) methods are already often used to drive systematic testing of software system configurations. However, a model of the system's configurations not conforming with respect to its software implementation, must be repaired in order to restore conformance. In this paper we extend CIT by devising a new search-based technique able to repair a model composed of a set of constraints among the various software system's parameters. Our technique can be used to detect and fix faults both in the model and in the real software system. Experiments for five real-world systems show that our approach can repair on average 37% of conformance faults. Moreover, we also show it can infer parameter constraints in a large real-world software system, hence it can be used for automated creation of CIT models.","PeriodicalId":312017,"journal":{"name":"2017 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW)","volume":"31 6-7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123650664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}