https://www.mdu.se/

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.

Software testing becomes more complex, more time-consuming, and more expensive. The risk that software errors remain undetected and cause critical failures increases. Consequently, in safety-critical development, testing software is standardized and it requires an engineer to show that tests fully exercise, or cover, the logic of the software. This method often requires a trained engineer to perform manual test generation, is prone to human error, and is expensive or impractical to use frequently in production. To overcome these issues, software testing needs to be performed earlier in the development process, more frequently, and aided by automated tools.

We devised an automated test generation tool called COMPLETETEST that avoids many of those problems. The method implemented in the tool and described in this thesis, works with software written in Function Block Diagram language, and can provide tests in just a few seconds. In addition, it does not rely on the expertise of a researcherspecialized in automated test generation and model checking. Although COMPLETETEST itself uses a model checker, a complex technique requiring a high level of expertise to generate tests, it provides a straightforward tabular interface to the intended users. In this way, its users do not need to learn the intricacies of using this approach such as how coverage criteria can be formalized and used by a model checker to automatically generate tests. If the technique can be demonstrated to work in production, it could detect and aid in the detection of errors in safety-critical software development, where conventional testing is not always applicable and efficient.

We conducted studies based on industrial use-case scenarios from Bombardier Transportation AB, showing how the approach can be applied to generate tests in software systems used in the safety-critical domain. To evaluate the approach, it was applied on real-world programs. The results indicate that it is efficient in terms of time required to generate tests and scales well for most of the software. There are still issues to resolve before the technique can be applied to more complex software, but we are already working on ways to overcome them. In particular, we need to understand how its usage in practice can vary depending on human and software process factors.