Detecting code smells using machine learning techniques: Are we there yet?
Code smells are symptoms of poor design and implementation choices weighing heavily on
the quality of produced source code. During the last decades several code smell detection …
the quality of produced source code. During the last decades several code smell detection …
Test smell detection tools: A systematic mapping study
Test smells are defined as sub-optimal design choices developers make when
implementing test cases. Hence, similar to code smells, the research community has …
implementing test cases. Hence, similar to code smells, the research community has …
An empirical evaluation of using large language models for automated unit test generation
Unit tests play a key role in ensuring the correctness of software. However, manually
creating unit tests is a laborious task, motivating the need for automation. Large Language …
creating unit tests is a laborious task, motivating the need for automation. Large Language …
No more manual tests? evaluating and improving chatgpt for unit test generation
Unit testing is essential in detecting bugs in functionally-discrete program units. Manually
writing high-quality unit tests is time-consuming and laborious. Although traditional …
writing high-quality unit tests is time-consuming and laborious. Although traditional …
[HTML][HTML] A3test: Assertion-augmented automated test case generation
Context: Test case generation is a critical yet challenging task in software development.
Recently, AthenaTest–a Deep Learning (DL) approach for generating unit test cases has …
Recently, AthenaTest–a Deep Learning (DL) approach for generating unit test cases has …
[PDF][PDF] Exploring the effectiveness of large language models in generating unit tests
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 (eg, GitHub Copilot) are …
code, or a combination of both. Although code generation models (eg, GitHub Copilot) are …
On the diffuseness and the impact on maintainability of code smells: a large scale empirical investigation
Code smells were defined as symptoms of poor design choices applied by programmers
during the development of a software project [2]. They might hinder the comprehensibility …
during the development of a software project [2]. They might hinder the comprehensibility …
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 (eg, GitHub Copilot) are …
code, or a combination of both. Although code generation models (eg, GitHub Copilot) are …
Effective test generation using pre-trained large language models and mutation testing
Context: One of the critical phases in the software development life cycle is software testing.
Testing helps with identifying potential bugs and reducing maintenance costs. The goal of …
Testing helps with identifying potential bugs and reducing maintenance costs. The goal of …
On the relation of test smells to software code quality
Test smells are sub-optimal design choices in the implementation of test code. As reported
by recent studies, their presence might not only negatively affect the comprehension of test …
by recent studies, their presence might not only negatively affect the comprehension of test …