Towards Automating Integration Testing of .NET Applications Using Roslyn

The increasing complexity and size of software products combined with pressure to have shorter time-to-market is making manual testing techniques too costly and unscalable. This is particularly observed in industrial systems where continuous integration and deployment are applied. Therefore, there is a growing need to automate the testing process and make it scalable with respect to the context of real-world and large industrial applications. While there are already some solutions for generation of unit level test cases, automatic generation of integration level test cases to verify interaction of software components poses specific challenges especially in object-oriented applications. In this paper, we describe our ongoing work in introducing a solution to automate generation of integration test cases for C# applications by exploiting the code analysis capabilities of Microsoft .NET compiler platform known as Roslyn. This is done in collaboration with ABB Industrial Automation Control Technologies (IACT) in Västerås-Sweden where the software for the ABB Ability™ 800xA distributed control system is developed.

I. Introduction

Software is gaining a rapidly growing role in the business model of many industries and companies, and lies at the core of Industry 4.0 concepts and principles. Examples of transitions towards software-based solutions can be clearly observed in automotive, telecommunication, and process industry domains where more and more tasks and functionality are implemented as and assigned to software components. On the other hand, this means that the quality of the embedded software can have direct impact on the quality of the final product. Therefore, appropriate testing methods are required to verify the correctness of the software. One major issue in testing industrial-scale software is that manual techniques are not scalable and practical anymore, and incur huge time and costs. For these reasons, industry is in need of testing techniques which can be automated, scale well, and fit as seamlessly and with minimum changes as possible in their current development processes.