Nirikshak: A Clustering Based Autonomous API Testing Framework
- URL: http://arxiv.org/abs/2112.08315v3
- Date: Wed, 22 Nov 2023 17:30:54 GMT
- Title: Nirikshak: A Clustering Based Autonomous API Testing Framework
- Authors: Yash Mahalwal, Pawel Pratyush, Yogesh Poonia
- Abstract summary: Nirikshak is a self-reliant testing framework for REST API testing.
It achieves level 2 of autonomy in executing REST API testing procedures.
Nirikshak is publicly available as an open-source software for the community at https://github.com/yashmahalwal/nirikshak.
- Score: 0.0
- License: http://creativecommons.org/licenses/by-nc-nd/4.0/
- Abstract: Quality Assurance (QA) is a critical component in product development,
particularly in software testing. Despite the evolution of automated methods,
testing for REST APIs often involves repetitive tasks. A significant portion of
resources is dedicated more to scripting tests than to detecting and resolving
actual software bugs. Additionally, conventional testing methods frequently
struggle to adapt to software updates. However, with advancements in data
science, a new paradigm is emerging: a self-reliant testing framework. This
innovative approach minimizes the need for user intervention, achieving level 2
of autonomy in executing REST API testing procedures. It does so by employing a
clustering method and analysis on logs categorizing test cases efficiently and
thereby streamlining the testing process as well as ensuring more dynamic
adaptability to software changes. Nirikshak is publicly available as an
open-source software for the community at
https://github.com/yashmahalwal/nirikshak.
Related papers
- Multi-language Unit Test Generation using LLMs [6.259245181881262]
We describe a generic pipeline that incorporates static analysis to guide LLMs in generating compilable and high-coverage test cases.
We show how the pipeline can be applied to different programming languages, specifically Java and Python, and to complex software requiring environment mocking.
Our results demonstrate that LLM-based test generation, when guided by static analysis, can be competitive with, and even outperform, state-of-the-art test-generation techniques in coverage achieved.
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) - KAT: Dependency-aware Automated API Testing with Large Language Models [1.7264233311359707]
KAT (Katalon API Testing) is a novel AI-driven approach that autonomously generates test cases to validate APIs.
Our evaluation of KAT using 12 real-world services shows that it can improve validation coverage, detect more undocumented status codes, and reduce false positives in these services.
arXiv Detail & Related papers (2024-07-14T14:48:18Z) - COTS: Connected OpenAPI Test Synthesis for RESTful Applications [0.0]
We introduce a (i) domain-specific language for OpenAPI specifications and (ii) a tool to support our methodology.
Our tool, dubbed COTS, generates (randomised) model-based test executions and reports software defects.
arXiv Detail & Related papers (2024-04-30T15:12:31Z) - Automating REST API Postman Test Cases Using LLM [0.0]
This research paper is dedicated to the exploration and implementation of an automated approach to generate test cases using Large Language Models.
The methodology integrates the use of Open AI to enhance the efficiency and effectiveness of test case generation.
The model that is developed during the research is trained using manually collected postman test cases or instances for various Rest APIs.
arXiv Detail & Related papers (2024-04-16T15:53:41Z) - Adaptive REST API Testing with Reinforcement Learning [54.68542517176757]
Current testing tools lack efficient exploration mechanisms, treating all operations and parameters equally.
Current tools struggle when response schemas are absent in the specification or exhibit variants.
We present an adaptive REST API testing technique incorporates reinforcement learning to prioritize operations during exploration.
arXiv Detail & Related papers (2023-09-08T20:27:05Z) - Towards Automatic Generation of Amplified Regression Test Oracles [44.45138073080198]
We propose a test oracle derivation approach to amplify regression test oracles.
The approach monitors the object state during test execution and compares it to the previous version to detect any changes in relation to the SUT's intended behaviour.
arXiv Detail & Related papers (2023-07-28T12:38:44Z) - ALBench: A Framework for Evaluating Active Learning in Object Detection [102.81795062493536]
This paper contributes an active learning benchmark framework named as ALBench for evaluating active learning in object detection.
Developed on an automatic deep model training system, this ALBench framework is easy-to-use, compatible with different active learning algorithms, and ensures the same training and testing protocols.
arXiv Detail & Related papers (2022-07-27T07:46:23Z) - SUPERNOVA: Automating Test Selection and Defect Prevention in AAA Video
Games Using Risk Based Testing and Machine Learning [62.997667081978825]
Testing video games is an increasingly difficult task as traditional methods fail to scale with growing software systems.
We present SUPERNOVA, a system responsible for test selection and defect prevention while also functioning as an automation hub.
The direct impact of this has been observed to be a reduction in 55% or more testing hours for an undisclosed sports game title.
arXiv Detail & Related papers (2022-03-10T00:47:46Z) - Beyond Accuracy: Behavioral Testing of NLP models with CheckList [66.42971817954806]
CheckList is a task-agnostic methodology for testing NLP models.
CheckList includes a matrix of general linguistic capabilities and test types that facilitate comprehensive test ideation.
In a user study, NLP practitioners with CheckList created twice as many tests, and found almost three times as many bugs as users without it.
arXiv Detail & Related papers (2020-05-08T15:48:31Z) - Dynamic Causal Effects Evaluation in A/B Testing with a Reinforcement
Learning Framework [68.96770035057716]
A/B testing is a business strategy to compare a new product with an old one in pharmaceutical, technological, and traditional industries.
This paper introduces a reinforcement learning framework for carrying A/B testing in online experiments.
arXiv Detail & Related papers (2020-02-05T10:25:02Z)
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.