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- Flemström, Daniel, 1971-
(författare)
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Similarity-Based Test Effort Reduction
- 2017
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Embedded computer systems are all around us. We find them in everything, from dishwashers to cars and airplanes. They must always work correctly and moreover, often within certain time constraints. The software of such a system can be very large and complex, e.g. in the case of a car or a train. Hence, we develop the software for embedded systems in smaller, manageable, parts. These parts can be successively integrated, until they form the complete software for the embedded system, possibly at different levels. This phase of the development process is called the system integration phase and is one of the most critical phases in the development of embedded systems. In this phase, substantial effort is spent on testing activities.Studies have found that a considerable amount of test effort is wasteful due to people, unknowingly or by necessity, performing similar (or even overlapping) test activities. Consequently, test cases may end up to be similar, partially or wholly. We identified such test similarities in a case study of 2500 test cases, written in natural language, from four different projects in the embedded vehicular domain. Such information can be used for reducing effort when maintaining or automating similar test cases.In another case study in the same domain, we investigated several approaches for prioritizing test cases to automate with the objective to reduce manual test effort as quick as possible given that similar automated tests could be reused (similarity-based reuse). We analyzed how the automation order affects the test effort for four projects with a total of 3919 integration test cases, written in natural language. The results showed that similarity-based reuse of automated test case script code, and the best-performing automation order can reduce the expected manual test effort with 20 percentage points.Another way of reducing test effort is to reuse test artifacts from one level of integration to another, instead of duplicating them. We studied such reuse methods, that we denote vertical reuse, in a systematic mapping study. While the results from of our systematic mapping study showed the viability of vertical test reuse methods, our industrial case studies showed that keeping track of similarities and test overlaps is both possible and feasible for test effort reduction. We further conclude that the test case automation order affects the manual test execution effort when there exist similar steps that cannot be removed, but are possible to reuse with respect to test script code.
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| 3. |
- Bosch, Jan, 1967, et al.
(författare)
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Accelerating digital transformation: 10 years of software center
- 2022
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Bok (övrigt vetenskapligt/konstnärligt)abstract
- This book celebrates the 10-year anniversary of Software Center (a collaboration between 18 European companies and five Swedish universities) by presenting some of the most impactful and relevant journal or conference papers that researchers in the center have published over the last decade. The book is organized around the five themes around which research in Software Center is organized, i.e. Continuous Delivery, Continuous Architecture, Metrics, Customer Data and Ecosystems Driven Development, and AI Engineering. The focus of the Continuous Delivery theme is to help companies to continuously build high quality products with the right degree of automation. The Continuous Architecture theme addresses challenges that arise when balancing the need for architectural quality and more agile ways of working with shorter development cycles. The Metrics theme studies and provides insight to understand, monitor and improve software processes, products and organizations. The fourth theme, Customer Data and Ecosystem Driven Development, helps companies make sense of the vast amounts of data that are continuously collected from products in the field. Eventually, the theme of AI Engineering addresses the challenge that many companies struggle with in terms of deploying machine- and deep-learning models in industrial contexts with production quality. Each theme has its own part in the book and each part has an introduction chapter and then a carefully selected reprint of the most important papers from that theme. This book mainly aims at researchers and advanced professionals in the areas of software engineering who would like to get an overview about the achievement made in various topics relevant for industrial large-scale software development and management - and to see how research benefits from a close cooperation between industry and academia.
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