Continuous Testing Improvement Model
Continuous Delivery is a practice where high-quality software is built in a way that it can be released into production at any time. However, systematic literature reviews and surveys performed as part of this Doctoral Research report that both the literature and the industry are still facing problems related to testing at using practices like Continuous Delivery or Continuous Development. Thus, we propose Continuous Testing Improvement Model (CTIM) as a solution to the testing problems in continuous software development environments. It brings together proposals and approaches from different authors which are presented as good practices grouped by type of tests and divided into four levels. These levels indicate an improvement hierarchy and an evolutionary path in the implementation of Continuous Testing. Also, an application called EvalCTIM was developed to support the appraisal of a testing process using the proposed model. Finally, in order to validate the model, an action-research methodology was employed through an interpretive theoretical evaluation followed by case studies conducted in real software development projects. After several improvements made as part of the validation outcomes, the results demonstrate that the model can be used as a solution for implementing Continuous Testing gradually at companies using Continuous Deployment or Continuous Delivery and measuring its progress.
Fri 21 MayDisplayed time zone: Amsterdam, Berlin, Bern, Rome, Stockholm, Vienna change
16:30 - 18:15 | Industrial case studies & Doctoral StudentsAST 2021 at AST Room Chair(s): Breno Miranda Federal University of Pernambuco | ||
16:30 15mShort-paper | Continuous Testing Improvement Model AST 2021 Maximiliano Agustin Mascheroni Universidad Nacional de La Plata, Emanuel Agustin Irrazábal Universidad Nacional del Nordeste, Gustavo Rossi Universidad Nacional de La Plata, LIFIA-Fac. Informatica, La Plata, Argentina Pre-print Media Attached | ||
16:45 30mLong-paper | Model-based Automation of Test Scripts Generation Across Product Variants: a Railway Perspective AST 2021 Alessio Bucaioni Mälardalen University, Fabio Di Silvestro Bombardier Railway Transportation, Inderjeet Singh Bombardier Railway Transportation, Mehrdad Saadatmand RISE Research Institutes of Sweden, Henry Muccini University of L'Aquila, Italy, Thorvaldur Jochumsson Bombardier Railway Transportation Pre-print Media Attached | ||
17:15 30mLong-paper | Using Machine Learning to Build Test Oracles: an Industrial Case Study on Elevators Dispatching Algorithms AST 2021 Aitor Arrieta University of Mondragon, Jon Ayerdi Mondragon Unibertsitatea, Miren Illarramendi Mondragon University, Aitor Agirre IKERLAN-IK4, Goiuria Sagardui University of Mondragon
, Maite Arratibel Orona Pre-print Media Attached | ||
17:45 30mLong-paper | Automatically Assessing and Extending Code Coverage for NPM Packages AST 2021 Haiyang Sun Università della Svizzera italiana, Andrea Rosà University of Lugano, Switzerland, Daniele Bonetta Oracle Labs, Walter Binder University of Lugano, Switzerland Media Attached |
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