Scalable and Efficient Test Suite Minimization using Large Language Models

Keskeiset käsitteet

A novel, scalable, and black-box test suite minimization approach (LTM) that leverages large pre-trained language models and vector-based similarity measures to efficiently identify and remove redundant test cases while maintaining high fault detection capability.

Tiivistelmä

The paper proposes LTM, a novel test suite minimization approach that addresses the scalability limitations of the state-of-the-art approach (ATM) by utilizing large pre-trained language models (LLMs) and vector-based similarity measures.
Key highlights:

LTM takes the source code of test cases as input, without requiring any preprocessing, and employs five different LLMs (CodeBERT, GraphCodeBERT, UniXcoder, StarEncoder, and CodeLlama) to generate test method embeddings.
LTM uses two vector-based similarity measures, Cosine Similarity and Euclidean Distance, to calculate the similarity between test method embeddings, which is more computationally efficient than the tree-based similarity measures used in ATM.
LTM employs a Genetic Algorithm (GA) to minimize test suites using the calculated similarity values as fitness.
LTM optimizes the GA search by utilizing a more efficient data structure to accelerate fitness calculation and enhance memory usage, leading to a 273-fold reduction in minimization time.
Experimental results on 17 Java projects with 835 versions show that the best configuration of LTM (UniXcoder/Cosine) outperforms ATM by achieving a slightly greater saving rate of testing time (41.72% versus 40.29%, on average), attaining a significantly higher fault detection rate (0.84 versus 0.81, on average), and minimizing test suites nearly five times faster on average, with higher gains for larger test suites and systems, thus achieving much higher scalability.

Tilastot

The average test execution time before test suite minimization is 1.58 minutes.
The average test execution time after test suite minimization using the best LTM configuration is 0.92 minutes.

Lainaukset

"LTM achieves high FDR results (an overall average FDR of 0.79 across configurations) for a 50% minimization budget (i.e., the percentage of test cases retained in the minimized test suite)."
"The best configuration of LTM is UniXcoder using Cosine similarity when considering both effectiveness (0.84 FDR on average) and efficiency (0.82 min on average), which also achieves a greater time saving rate (an average TSR of 41.72%)."
"For the large project, Closure, UniXcoder using Cosine Similarity takes only 17.90 min in terms of MT and achieves an FDR of 0.79, while saving 52.55% of testing time."

Tärkeimmät oivallukset

LTM: Scalable and Black-box Similarity-based Test Suite Minimization based on Language Models

by Rongqi Pan,T... klo arxiv.org 05-06-2024

https://arxiv.org/pdf/2304.01397.pdf

Syvällisempiä Kysymyksiä

How can the performance of LTM be further improved by incorporating additional information, such as code comments or test case execution history, into the language model-based embeddings?

Incorporating additional information like code comments or test case execution history into the language model-based embeddings can enhance the performance of LTM in several ways:

Contextual Understanding: Including code comments can provide valuable context to the language model, helping it better understand the purpose and functionality of the test code. This contextual information can lead to more accurate embeddings that capture the intent behind the test cases.

Improved Semantic Understanding: By incorporating test case execution history, the language model can learn from past test outcomes and patterns. This historical data can help the model identify relationships between test cases and faults, leading to more informed similarity measurements and better fault detection rates.

Enhanced Diversity: Utilizing information from test case execution history can aid in selecting a diverse set of test cases for minimization. By considering the outcomes of previous test executions, the model can prioritize retaining test cases that have historically been effective in detecting faults, thus improving the fault detection capability of the minimized test suite.

Fine-tuning Embeddings: By incorporating code comments and execution history, the language model can fine-tune its embeddings to better represent the specific characteristics of the test cases in the given software context. This fine-tuning can lead to more tailored and effective similarity measurements during the minimization process.

What are the potential limitations of using pre-trained language models for test suite minimization, and how can they be addressed?

While pre-trained language models offer significant benefits for test suite minimization, they also come with certain limitations:

Domain Specificity: Pre-trained language models may not be specifically tailored to the software testing domain, which can limit their ability to capture the nuances and intricacies of test case code. This lack of domain-specific knowledge may result in suboptimal embeddings and similarity measurements.

Data Bias: Language models are trained on large datasets, which may introduce biases that are not representative of the software being tested. Biases in the training data can lead to skewed embeddings and inaccurate similarity calculations, impacting the effectiveness of test suite minimization.

Scalability: Processing large volumes of test case code using pre-trained language models can be computationally intensive and time-consuming. The scalability of the approach may be hindered when dealing with extensive test suites or complex software systems.

To address these limitations, several strategies can be employed:

Fine-tuning: Fine-tuning the pre-trained language models on a specific software testing dataset can help adapt the embeddings to the domain-specific characteristics of test case code, improving their relevance and effectiveness for test suite minimization.

Data Augmentation: Augmenting the training data with additional examples from the software testing domain can help mitigate biases in the pre-trained models and enhance their understanding of test case code.

Model Customization: Customizing the architecture or training objectives of the language models to better suit the requirements of test suite minimization can lead to more accurate embeddings and similarity measurements.

Hybrid Approaches: Combining pre-trained language models with domain-specific features or techniques, such as code parsing algorithms or test case clustering methods, can complement the capabilities of the models and address their limitations in certain scenarios.

How can the proposed approach be extended to support other software engineering tasks beyond test suite minimization, such as test case selection or prioritization?

The proposed approach based on pre-trained language models can be extended to support various other software engineering tasks beyond test suite minimization, such as test case selection or prioritization, by leveraging the capabilities of the models in different ways:

Test Case Selection: For test case selection, the language models can be used to identify the most relevant and critical test cases based on their embeddings and similarity measurements. By considering the similarities between test cases and their impact on fault detection, the models can assist in selecting a subset of test cases that provide maximum coverage and effectiveness.

Test Case Prioritization: Language model-based embeddings can be utilized to prioritize test cases based on their importance and likelihood of detecting faults. By analyzing the similarities between test cases and their historical performance, the models can help prioritize the execution order of test cases to optimize testing resources and maximize fault detection efficiency.

Automated Test Generation: The language models can also be employed for automated test generation by leveraging their understanding of code patterns and semantics. By generating new test cases based on existing code and test case embeddings, the models can assist in expanding test coverage and enhancing the overall quality of the testing process.

Bug Localization: Language model-based embeddings can aid in bug localization by analyzing the similarities between code snippets and fault-inducing changes. By comparing the embeddings of code segments, the models can help identify the potential locations of bugs and streamline the debugging process.

By adapting the language model-based approach and incorporating domain-specific features and requirements, it can be extended to various software engineering tasks to improve efficiency, accuracy, and automation in the testing and development processes.