TestSpark: IntelliJ IDEA's Ultimate Test Generation Companion

• URL: http://arxiv.org/abs/2401.06580v1
• Date: Fri, 12 Jan 2024 13:53:57 GMT
• Title: TestSpark: IntelliJ IDEA's Ultimate Test Generation Companion
• Authors: Arkadii Sapozhnikov, Mitchell Olsthoorn, Annibale Panichella, Vladimir Kovalenko, Pouria Derakhshanfar
• Abstract summary: This paper introduces TestSpark, a plugin for IntelliJ IDEA that enables users to generate unit tests with only a few clicks. TestSpark also allows users to easily modify and run each generated test and integrate them into the project workflow.
• Score: 15.13443954421825
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Writing software tests is laborious and time-consuming. To address this, prior studies introduced various automated test-generation techniques. A well-explored research direction in this field is unit test generation, wherein artificial intelligence (AI) techniques create tests for a method/class under test. While many of these techniques have primarily found applications in a research context, existing tools (e.g., EvoSuite, Randoop, and AthenaTest) are not user-friendly and are tailored to a single technique. This paper introduces TestSpark, a plugin for IntelliJ IDEA that enables users to generate unit tests with only a few clicks directly within their Integrated Development Environment (IDE). Furthermore, TestSpark also allows users to easily modify and run each generated test and integrate them into the project workflow. TestSpark leverages the advances of search-based test generation tools, and it introduces a technique to generate unit tests using Large Language Models (LLMs) by creating a feedback cycle between the IDE and the LLM. Since TestSpark is an open-source (https://github.com/JetBrains-Research/TestSpark), extendable, and well-documented tool, it is possible to add new test generation methods into the plugin with the minimum effort. This paper also explains our future studies related to TestSpark and our preliminary results. Demo video: https://youtu.be/0F4PrxWfiXo

Related papers

• ProjectTest: A Project-level LLM Unit Test Generation Benchmark and Impact of Error Fixing Mechanisms [48.43237545197775]
  Unit test generation has become a promising and important use case of LLMs. ProjectTest is a project-level benchmark for unit test generation covering Python, Java, and JavaScript.
  arXiv  Detail & Related papers  (2025-02-10T15:24:30Z)
• Learning to Generate Unit Tests for Automated Debugging [52.63217175637201]
  Unit tests (UTs) play an instrumental role in assessing code correctness as well as providing feedback to a large language model (LLM) We propose UTGen, which teaches LLMs to generate unit test inputs that reveal errors along with their correct expected outputs. We show that UTGen outperforms UT generation baselines by 7.59% based on a metric measuring the presence of both error-revealing UT inputs and correct UT outputs.
  arXiv  Detail & Related papers  (2025-02-03T18:51:43Z)
• LlamaRestTest: Effective REST API Testing with Small Language Models [50.058600784556816]
  We present LlamaRestTest, a novel approach that employs two custom LLMs to generate realistic test inputs. LlamaRestTest surpasses state-of-the-art tools in code coverage and error detection, even with RESTGPT-enhanced specifications.
  arXiv  Detail & Related papers  (2025-01-15T05:51:20Z)
• Commit0: Library Generation from Scratch [77.38414688148006]
  Commit0 is a benchmark that challenges AI agents to write libraries from scratch. Agents are provided with a specification document outlining the library's API as well as a suite of interactive unit tests. Commit0 also offers an interactive environment where models receive static analysis and execution feedback on the code they generate.
  arXiv  Detail & Related papers  (2024-12-02T18:11:30Z)
• ASTER: Natural and Multi-language Unit Test Generation with LLMs [6.259245181881262]
  We describe a generic pipeline that incorporates static analysis to guide LLMs in generating compilable and high-coverage test cases. We conduct an empirical study to assess the quality of the generated tests in terms of code coverage and test naturalness.
  arXiv  Detail & Related papers  (2024-09-04T21:46:18Z)
• A System for Automated Unit Test Generation Using Large Language Models and Assessment of Generated Test Suites [1.4563527353943984]
  Large Language Models (LLMs) have been applied to various aspects of software development. We present AgoneTest: an automated system for generating test suites for Java projects.
  arXiv  Detail & Related papers  (2024-08-14T23:02:16Z)
• Observation-based unit test generation at Meta [52.4716552057909]
  TestGen automatically generates unit tests, carved from serialized observations of complex objects, observed during app execution. TestGen has landed 518 tests into production, which have been executed 9,617,349 times in continuous integration, finding 5,702 faults. Our evaluation reveals that, when carving its observations from 4,361 reliable end-to-end tests, TestGen was able to generate tests for at least 86% of the classes covered by end-to-end tests.
  arXiv  Detail & Related papers  (2024-02-09T00:34:39Z)
• PyTester: Deep Reinforcement Learning for Text-to-Testcase Generation [20.441921569948562]
  Test-driven development (TDD) mandates writing test cases based on requirements before writing the actual code. While writing test cases is the centerpiece of TDD, it is time-consuming, expensive, and often shunned by developers. We introduce PyTester, a Text-to-Testcase generation approach that can automatically generate correct, executable, complete, and effective test cases.
  arXiv  Detail & Related papers  (2024-01-15T10:21:58Z)
• Do Automatic Test Generation Tools Generate Flaky Tests? [12.813573907094074]
  The prevalence and nature of flaky tests produced by test generation tools remain largely unknown. We generate tests using EvoSuite (Java) and Pynguin (Python) and execute each test 200 times. Our results show that flakiness is at least as common in generated tests as in developer-written tests.
  arXiv  Detail & Related papers  (2023-10-08T16:44:27Z)
• Learning Deep Semantics for Test Completion [46.842174440120196]
  We formalize the novel task of test completion to automatically complete the next statement in a test method based on the context of prior statements and the code under test. We develop TeCo -- a deep learning model using code semantics for test completion.
  arXiv  Detail & Related papers  (2023-02-20T18:53:56Z)
• Automated Support for Unit Test Generation: A Tutorial Book Chapter [21.716667622896193]
  Unit testing is a stage of testing where the smallest segment of code that can be tested in isolation from the rest of the system is tested. Unit tests are typically written as executable code, often in a format provided by a unit testing framework such as pytest for Python. This chapter introduces the concept of search-based unit test generation.
  arXiv  Detail & Related papers  (2021-10-26T11:13:40Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.