Open Access

ARTICLE

Test Case Generation Evaluator for the Implementation of Test Case Generation Algorithms Based on Learning to Rank

by Zhonghao Guo^*, Xinyue Xu, Xiangxian Chen

College of Biomedical Engineering & Instrument Science, Zhejiang University, Hangzhou, 310000, China

* Corresponding Author: Zhonghao Guo. Email:

(This article belongs to the Special Issue: Emerging Artificial Intelligence Techniques for Software Engineering Process Optimization)

Computer Systems Science and Engineering 2024, 48(2), 479-509. https://doi.org/10.32604/csse.2023.043932

Received 16 July 2023; Accepted 04 December 2023; Issue published 19 March 2024

Abstract

In software testing, the quality of test cases is crucial, but manual generation is time-consuming. Various automatic test case generation methods exist, requiring careful selection based on program features. Current evaluation methods compare a limited set of metrics, which does not support a larger number of metrics or consider the relative importance of each metric to the final assessment. To address this, we propose an evaluation tool, the Test Case Generation Evaluator (TCGE), based on the learning to rank (L2R) algorithm. Unlike previous approaches, our method comprehensively evaluates algorithms by considering multiple metrics, resulting in a more reasoned assessment. The main principle of the TCGE is the formation of feature vectors that are of concern by the tester. Through training, the feature vectors are sorted to generate a list, with the order of the methods on the list determined according to their effectiveness on the tested assembly. We implement TCGE using three L2R algorithms: Listnet, LambdaMART, and RFLambdaMART. Evaluation employs a dataset with features of classical test case generation algorithms and three metrics—Normalized Discounted Cumulative Gain (NDCG), Mean Average Precision (MAP), and Mean Reciprocal Rank (MRR). Results demonstrate the TCGE’s superior effectiveness in evaluating test case generation algorithms compared to other methods. Among the three L2R algorithms, RFLambdaMART proves the most effective, achieving an accuracy above 96.5%, surpassing LambdaMART by 2% and Listnet by 1.5%. Consequently, the TCGE framework exhibits significant application value in the evaluation of test case generation algorithms.

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Keywords

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