Zoltán Micskei

Dávid Honfi, Zoltán Micskei

Context. White-box test generation is a technique used for automatically selecting test inputs using only the source or binary code. However, such techniques encounter challenges when applying them to complex programs. One of the main challenges is handling the dependencies of the unit under test. Objective. Without proper actions, generated tests cannot cover all parts of the source code, or calling the dependencies may cause unexpected side effects (e.g., file system or network access). These issues should be tackled while maintaining the advantages of white-box test generation. Method. In this paper, we present an automated source code transformation approach tackling the dependency issue for white-box test generation. This technique isolates the test execution by creating a parameterized sandbox wrapped around the transformed unit. We implemented the approach in a ready-to-use tool using Microsoft Pex as a test generator, and evaluated it on 10 open-source projects from GitHub having more than 38.000 lines of code in total. Results. The results from the evaluation indicate that if the lack of isolation hinders white-box test generation, then our approach is able to help: it increases the code coverage reached by the automatically generated test, while it reduces unwanted side effects. Also, our results act as a unique baseline for the test generation performance of Microsoft Pex on open-source projects. Conclusion. Based on the results, our source code transformations might serve well for alleviating the isolation problem in white-box test generation as it increases the coverage reached in such situations, while maintaining the practical applicability of the tests generated on the isolated code.

Gyula Sallai, Ákos Hajdu, Tamás Tóth et al.

Formal verification techniques are widely used for detecting design flaws in software systems. Formal verification can be done by transforming an already implemented source code to a formal model and attempting to prove certain properties of the model (e.g. that no erroneous state can occur during execution). Unfortunately, transformations from source code to a formal model often yield large and complex models, making the verification process inefficient and costly. In order to reduce the size of the resulting model, optimization transformations can be used. Such optimizations include common algorithms known from compiler design and different program slicing techniques. Our paper describes a framework for transforming C programs to a formal model, enhanced by various optimizations for size reduction. We evaluate and compare several optimization algorithms regarding their effect on the size of the model and the efficiency of the verification. Results show that different optimizations are more suitable for certain models, justifying the need for a framework that includes several algorithms.