# Generating Class-Level Integration Tests Using Call Site Information

arXiv: https://arxiv.org/abs/2001.04221 · HTML: https://mlsanity.com/paper/2001.04221/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 13 Jan 2020.

## Built on by (most cited first, 1 of 1)

- [Leveraging Large Language Models for Enhancing the Understandability of Generated Unit Tests](https://mlsanity.com/paper/2408.11710/) (2024)

## Abstract (arXiv)

Search-based approaches have been used in the literature to automate the process of creating unit test cases. However, related work has shown that generated unit-tests with high code coverage could be ineffective, i.e., they may not detect all faults or kill all injected mutants. In this paper, we propose CLING, an integration-level test case generation approach that exploits how a pair of classes, the caller and the callee, interact with each other through method calls. In particular, CLING generates integration-level test cases that maximize the Coupled Branches Criterion (CBC). Coupled branches are pairs of branches containing a branch of the caller and a branch of the callee such that an integration test that exercises the former also exercises the latter. CBC is a novel integration-level coverage criterion, measuring the degree to which a test suite exercises the interactions between a caller and its callee classes. We implemented CLING and evaluated the approach on 140 pairs of classes from five different open-source Java projects. Our results show that (1) CLING generates test suites with high CBC coverage, thanks to the definition of the test suite generation as a many-objectives problem where each couple of branches is an independent objective; (2) such generated suites trigger different class interactions and can kill on average 7.7% (with a maximum of 50%) of mutants that are not detected by tests generated at the unit level; (3) CLING can detect integration faults coming from wrong assumptions about the usage of the callee class (32 for our subject systems) that remain undetected when using automatically generated unit-level test suites.