Automated Support for Unit Test Generation: A Tutorial Book Chapter

TL;DR

This paper introduces search-based AI techniques for automating the generation of unit tests, aiming to reduce manual effort and improve code coverage in software testing.

Contribution

It presents two algorithms that generate pytest-formatted unit tests using optimization and metaheuristic search, advancing automated test generation methods.

Findings

Algorithms effectively generate unit tests with high code coverage.

Search-based approach reduces manual effort in test creation.

Framework supports integration with existing testing tools.

Abstract

Unit testing is a stage of testing where the smallest segment of code that can be tested in isolation from the rest of the system - often a class - is tested. Unit tests are typically written as executable code, often in a format provided by a unit testing framework such as pytest for Python. Creating unit tests is a time and effort-intensive process with many repetitive, manual elements. To illustrate how AI can support unit testing, this chapter introduces the concept of search-based unit test generation. This technique frames the selection of test input as an optimization problem - we seek a set of test cases that meet some measurable goal of a tester - and unleashes powerful metaheuristic search algorithms to identify the best possible test cases within a restricted timeframe. This chapter introduces two algorithms that can generate pytest-formatted unit tests, tuned towards coverage of source code statements. The chapter concludes by discussing more advanced concepts and gives pointers to further reading for how artificial intelligence can support developers and testers when unit testing software.