| 1. |
- Doganay, Kivanc
(författare)
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Applications of Optimization Methods in Industrial Maintenance Scheduling and Software Testing
- 2014
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- As the world is getting more and more competitive, efficiency has become a bigger concern than ever for many businesses. Certain efficiency concerns can naturally be expressed as optimization problems, which is a well studied field in the academia. However, optimization algorithms are not as widely employed in industrial practice as they could. There are various reasons for the lack of widespread adoption. For example, it can be difficult or even impossible for non-experts to formulate a detailed mathematical model of the problem. On the other hand, a scientist usually does not have a deep enough understanding of critical business details, and may fail to capture enough details of the real- world phenomenon of concern. While a model at an arbitrary abstraction level is often good enough to demonstrate the optimization approach, ignoring relevant aspects can easily render the solution impractical for the industry. This is an important problem, because applicability concerns hinder the possible gains that can be achieved by using the academic knowledge in industrial practice. In this thesis, we study the challenges of industrial optimization problems in the form of four case studies at four different companies, in the domains of maintenance schedule optimization and search-based software testing. Working with multiple case studies in different domains allows us to better understand the possible gains and practical challenges in applying optimization methods in an industrial setting. Often there is a need to trade precision for applicability, which is typically very context dependent. Therefore, we compare our results against base values, e.g., results from simpler algorithms or the state of the practice in the given context, where applicable. Even though we cannot claim that optimization methods are applicable in all situations, our work serves as an empirical evidence for the usability of optimization methods for improvements in different industrial contexts. We hope that our work can encourage the adoption of optimization techniques by more industrial practitioners.
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| 2. |
- Doganay, Kivanc, et al.
(författare)
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Search-Based Testing for Embedded Telecom Software with Complex Input Structures
- 2014
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Ingår i: Testing Software and Systems (Ictss 2014). - Berlin, Heidelberg : Springer. - 9783662448571 ; , s. 205-210
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Konferensbidrag (refereegranskat)abstract
- In this paper, we discuss the application of search-based software testing techniques for unit level testing of a real-world telecommunication middleware at Ericsson. Our current implementation analyzes the existing test cases to handle non-trivial variables such as uninitialized pointers, and to discover any setup code that needs to run before the actual test case, such as setting global system parameters. Hill climbing (HC) and (1+1) evolutionary algorithm (EA) metaheuristic search algorithms are used to generate input data for branch coverage. We compare HC, (1+1) EA, and random search with respect to effectiveness, measured as branch coverage, and efficiency, measured as number of executions needed. Difficulties arising from the specialized execution environment and the adaptations for handling these problems are also discussed.
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| 3. |
- Doganay, Kivanc, et al.
(författare)
-
Search-based Testing for Embedded Telecommunication Software with Complex Input Structures: An Industrial Case Study
- 2014. - 6
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Rapport (övrigt vetenskapligt/konstnärligt)abstract
- In this paper, we discuss the application of search-based software testing techniques for unit level testing of a real-world telecommunication middleware at Ericsson. Input data for the system under test consists of nested data structures, and includes non-trivial variables such as uninitialized pointers. Our current implementation analyzes the existing test cases to discover how to handle pointers, set global system parameters, and any other setup code that needs to run before the actual test case. Hill climbing (HC) and (1+1) evolutionary algorithm (EA) metaheuristic search algorithms are used to generate input data for branch coverage. We compare HC, (1+1)EA, and random search as a baseline of performance with respect to effectiveness, measured as branch coverage, and efficiency, measured as number of executions needed. Difficulties arising from the specialized execution environment and the adaptations for handling these problems are also discussed.
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