# Does Diversity Improve the Test Suite Generation for Mobile Applications?

## TL;DR

This paper explores how diversity in search heuristics can improve test suite generation for mobile apps by analyzing fitness landscapes and adapting heuristics accordingly.

## Contribution

It introduces SAPIENZ^div, an adapted heuristic that preserves diversity during search, based on fitness landscape analysis of the original SAPIENZ approach.

## Key findings

- SAPIENZ^div outperforms the original SAPIENZ in test suite quality.
- Diversity preservation improves search effectiveness.
- Fitness landscape analysis informs better heuristic design.

## Abstract

In search-based software engineering we often use popular heuristics with default configurations, which typically lead to suboptimal results, or we perform experiments to identify configurations on a trial-and-error basis, which may lead to better results for a specific problem. To obtain better results while avoiding trial-and-error experiments, a fitness landscape analysis is helpful in understanding the search problem, and making an informed decision about the heuristics. In this paper, we investigate the search problem of test suite generation for mobile applications (apps) using SAPIENZ whose heuristic is a default NSGA-II. We analyze the fitness landscape of SAPIENZ with respect to genotypic diversity and use the gained insights to adapt the heuristic of SAPIENZ. These adaptations result in SAPIENZ^div that aims for preserving the diversity of test suites during the search. To evaluate SAPIENZ^div, we perform a head-to-head comparison with SAPIENZ on 76 open-source apps.