arXiv subjects

Joanna C. S. Santos

Publications and source records attributed to Joanna C. S. Santos.

22 records · Page 2Linked to original sources

arXiv2024

Using Large Language Models to Generate JUnit Tests: An Empirical Study

A code generation model generates code by taking a prompt from a code comment, existing code, or a combination of both. Although code generation models (e.g., GitHub Copilot) are increasingly being adopted in practice, it is unclear whether they can successfully be used for unit test generation without fine-tuning for a strongly typed language like Java. To fill this gap, we investigated how well three models (Codex, GPT-3.5-Turbo, and StarCoder) can generate unit tests. We used two benchmarks (HumanEval and Evosuite SF110) to investigate the effect of context generation on the unit test generation process. We evaluated the models based on compilation rates, test correctness, test coverage, and test smells. We found that the Codex model achieved above 80% coverage for the HumanEval dataset, but no model had more than 2% coverage for the EvoSuite SF110 benchmark. The generated tests also suffered from test smells, such as Duplicated Asserts and Empty Tests.

cs.SE↗

arXiv2023

Enhancing Automated Program Repair through Fine-tuning and Prompt Engineering

Sequence-to-sequence models have been used to transform erroneous programs into correct ones when trained with a large enough dataset. Some recent studies also demonstrated strong empirical evidence that code review could improve the program repair further. Large language models, trained with Natural Language (NL) and Programming Language (PL), can contain inherent knowledge of both. In this study, we investigate if this inherent knowledge of PL and NL can be utilized to improve automated program repair. We applied PLBART and CodeT5, two state-of-the-art language models that are pre-trained with both PL and NL, on two such natural language-based program repair datasets and found that the pre-trained language models fine-tuned with datasets containing both code review and subsequent code changes notably outperformed each of the previous models. With the advent of code generative models like Codex and GPT-3.5-Turbo, we also performed zero-shot and few-shots learning-based prompt engineering to assess their performance on these datasets. However, the practical application of using LLMs in the context of automated program repair is still a long way off based on our manual analysis of the generated repaired codes by the learning models.

cs.LG↗

arXiv2021

ArCode: Facilitating the Use of Application Frameworks to Implement Tactics and Patterns

Software designers and developers are increasingly relying on application frameworks as first-class design concepts. They instantiate the services that frameworks provide to implement various architectural tactics and patterns. One of the challenges in using frameworks for such tasks is the difficulty of learning and correctly using frameworks' APIs. This paper introduces a learning-based approach called ArCode to help novice programmers correctly use frameworks' APIs to implement architectural tactics and patterns. ArCode has several novel components: a graph-based approach for learning specification of a framework from a limited number of training software, a program analysis algorithm to eliminate erroneous training data, and a recommender module to help programmers use APIs correctly and identify API misuses in their programs. We evaluated our technique across two popular frameworks: JAAS security framework used for authentication and authorization tactic and Java RMI framework used to enable remote method invocation between client and server and other object-oriented patterns. Our evaluation results show (i) the feasibility of using ArCode to learn the specification of a framework; (ii) ArCode generates accurate recommendations for finding the next API call to implement an architectural tactic/pattern based on the context of the programmer's code; (iii) it accurately detects API misuses in the code that implements a tactic/pattern and provides fix recommendations. Comparison of ArCode with two prior techniques (MAPO and GrouMiner) on API recommendation and misuse detection shows that ArCode outperforms these approaches.

cs.SE↗

arXiv2017

A Large-Scale Study on the Usage of Testing Patterns that Address Maintainability Attributes (Patterns for Ease of Modification, Diagnoses, and Comprehension)

Test case maintainability is an important concern, especially in open source and distributed development environments where projects typically have high contributor turnover with varying backgrounds and experience, and where code ownership changes often. Similar to design patterns, patterns for unit testing promote maintainability quality attributes such as ease of diagnoses, modifiability, and comprehension. In this paper, we report the results of a large-scale study on the usage of four xUnit testing patterns which can be used to satisfy these maintainability attributes. This is a first-of-its-kind study which developed automated techniques to investigate these issues across 82,447 open source projects, and the findings provide more insight into testing practices in open source projects. Our results indicate that only 17% of projects had test cases, and from the 251 testing frameworks we studied, 93 of them were being used. We found 24% of projects with test files implemented patterns that could help with maintainability, while the remaining did not use these patterns. Multiple qualitative analyses indicate that usage of patterns was an ad-hoc decision by individual developers, rather than motivated by the characteristics of the project, and that developers sometimes used alternative techniques to address maintainability concerns.

cs.SE↗