Predicting Code Coverage without Execution (2307.13383v1)

Abstract: Code coverage is a widely used metric for quantifying the extent to which program elements, such as statements or branches, are executed during testing. Calculating code coverage is resource-intensive, requiring code building and execution with additional overhead for the instrumentation. Furthermore, computing coverage of any snippet of code requires the whole program context. Using Machine Learning to amortize this expensive process could lower the cost of code coverage by requiring only the source code context, and the task of code coverage prediction can be a novel benchmark for judging the ability of models to understand code. We propose a novel benchmark task called Code Coverage Prediction for LLMs. We formalize this task to evaluate the capability of LLMs in understanding code execution by determining which lines of a method are executed by a given test case and inputs. We curate and release a dataset we call COVERAGEEVAL by executing tests and code from the HumanEval dataset and collecting code coverage information. We report the performance of four state-of-the-art LLMs used for code-related tasks, including OpenAI's GPT-4 and GPT-3.5-Turbo, Google's BARD, and Anthropic's Claude, on the Code Coverage Prediction task. Finally, we argue that code coverage as a metric and pre-training data source are valuable for overall LLM performance on software engineering tasks.

• The paper proposes a novel machine learning approach that predicts code coverage from static analysis, bypassing the need for execution.
• It evaluates four LLMs, including GPT-4, demonstrating promising accuracy in mirroring dynamic execution patterns.
• The approach offers potential to reduce computational overhead in testing and enables integration into CI/CD pipelines.

An Essay on "Predicting Code Coverage without Execution"

The paper "Predicting Code Coverage without Execution" by Tufano, Chandel, Agarwal, Sundaresan, and Clement proposes an innovative approach to code coverage computation by utilizing machine learning techniques. The research introduces a novel benchmark task, Code Coverage Prediction, aimed at evaluating LLMs in comprehending code execution without requiring actual code execution. The paper is situated within the broader context of leveraging AI to optimize standard software engineering practices, particularly those that are computationally or resource-intensive.

Overview of Code Coverage and Challenges

Code coverage is a well-established metric in software testing, used to measure how much of the code is exercised by test suites. It renders a quantifiable gauge of test quality. However, traditional code coverage computation is an expensive process requiring code instrumentation, building, and execution. This becomes particularly cumbersome in large software systems or when full code contexts are unavailable. Herein lies the impetus for the authors’ exploration into utilizing LLMs to predict code coverage, thus bypassing traditional execution-based methods.

The Code Coverage Prediction Task

The authors formalize a Code Coverage Prediction task to assess the degree to which LLMs can discern code execution dynamics from static code analysis. The task involves predicting which lines of code in a given method are executed for a given test case. A dataset was developed from the HumanEval dataset, including tests and corresponding code coverage data. This task serves not only as a performance benchmark for LLMs but also offers practical implications where actual execution is not feasible.

Evaluation of LLMs

The paper presents an empirical evaluation of four advanced LLMs: OpenAI's GPT-4, GPT-3.5-Turbo, Google's BARD, and Anthropic's Claude on the Code Coverage Prediction task. Performance was assessed using various metrics, including exact sequence match, statement correctness, and branch correctness. Notably, OpenAI's GPT-4 emerged as the most proficient, although the performance across all models indicates that the accurate prediction of code coverage remains challenging, especially concerning complex branch statements.

Implications and Speculations on Future Developments

The implications of this research are multifaceted. Practically, a successful LLM-based code coverage predictor could alleviate the computational burden of traditional approaches, offering a chance to integrate live coverage analysis in development environments and CI/CD pipelines. Theoretically, the task highlights the scope of LLMs not just as generators of syntactically correct code, but as entities capable of grasping deeper code execution semantics.

Furthermore, the authors propose using code coverage prediction tasks as a pre-training objective for LLMs. This could potentially enhance the models’ understanding of execution semantics, enriching their performance on various downstream tasks in the domain of code analysis and generation.

Concluding Thoughts

This research elucidates significant advancements and challenges in applying LLMs to predict code coverage without execution. While current results demonstrate promise, they underscore the complexity of truly capturing execution semantics purely from static analysis. The introduction of such tasks paves the way for a deeper fusion of AI and empirical software testing methodologies, pointing toward rich future research avenues in developing more intelligent software engineering tools.

In summary, through the lens of machine learning, this work re-examines conventional software testing metrics, introducing innovative perspectives on how modern AI can reshape these practices, offering efficiency and novel capabilities in the software development lifecycle.