Skip to main content

LUP Student Papers

LUND UNIVERSITY LIBRARIES

Optimizing Quality Assurance Process through Test Data Analysis

Malmborg, Johanna LU and Niklasson, Ellen (2025) EITL05 20251
Department of Electrical and Information Technology
Abstract (Swedish)
Detta examensarbete syftar till att utvärdera om användningen av historisk testdata kan bidra till en förbättrad testprocess. I rapporten presenteras två JavaScript-algoritmer utvecklade av uppsatts- författarna, createTestRun och updateTestRun, som är specifikt utvecklade för att optimera urvalet av testfall och minimera redundans mellan testkörningar. Algoritmernas bygger på historisk data som hämtats via befintliga APIer, där createTestRun automatiskt skapar nya testrundor och updateTestRun uppdaterar en befintlig testrunda baserat på andra testrundors testkörningar. Dessa algoritmer karakteriseras av en kombination av filtrering och prioritering, där testfall rangordnas baserat på deras tidigare exekveringshistorik och... (More)
Detta examensarbete syftar till att utvärdera om användningen av historisk testdata kan bidra till en förbättrad testprocess. I rapporten presenteras två JavaScript-algoritmer utvecklade av uppsatts- författarna, createTestRun och updateTestRun, som är specifikt utvecklade för att optimera urvalet av testfall och minimera redundans mellan testkörningar. Algoritmernas bygger på historisk data som hämtats via befintliga APIer, där createTestRun automatiskt skapar nya testrundor och updateTestRun uppdaterar en befintlig testrunda baserat på andra testrundors testkörningar. Dessa algoritmer karakteriseras av en kombination av filtrering och prioritering, där testfall rangordnas baserat på deras tidigare exekveringshistorik och godkännandestatus. Vid utvärdering av algoritmerna kan det konstateras att en statistikbaserad selektion av testfall kan leda till en betydande minskning av testrundans storlek. Resultatet är lovande for tidsbesparing, resursallokering och erbjuder en skalbar lösning för storskaliga testrundor. (Less)
Abstract
This thesis presents an approach to utilize historical test execution data for improving the test process within a Quality Assurance (QA) department. The thesis research introduces two custom-built JavaScript algorithms, createTestRun and updateTestRun, designed to optimize the selection of test cases and effectively reduce redundancy in between test runs. The algorithms were developed by the thesis authors and operate on data retrieved from an existing test management API, where the createTestRun algorithm automatically creates test runs and the updateTestRun algorithm updates an existing test run based on the execution history of other test runs. The data collected are filtered and prioritized based on when they were previously executed... (More)
This thesis presents an approach to utilize historical test execution data for improving the test process within a Quality Assurance (QA) department. The thesis research introduces two custom-built JavaScript algorithms, createTestRun and updateTestRun, designed to optimize the selection of test cases and effectively reduce redundancy in between test runs. The algorithms were developed by the thesis authors and operate on data retrieved from an existing test management API, where the createTestRun algorithm automatically creates test runs and the updateTestRun algorithm updates an existing test run based on the execution history of other test runs. The data collected are filtered and prioritized based on when they were previously executed or approved. After evaluation of the developed algorithms, it is concluded that the research demonstrates how targeted test selection and filtering historical data can significantly reduce the test scope. The result shows a promising growth in time savings, allocation of resources and offering a scalable solution to large-scale test runs. (Less)
Please use this url to cite or link to this publication:
author
Malmborg, Johanna LU and Niklasson, Ellen
supervisor
organization
course
EITL05 20251
year
type
M2 - Bachelor Degree
subject
keywords
Regression testing, Historical data, JavaScript, API, Test Optimization
report number
LU/LTH-EIT 2025-1065
language
English
id
9199032
date added to LUP
2025-06-17 15:18:39
date last changed
2025-06-17 15:18:39
@misc{9199032,
  abstract     = {{This thesis presents an approach to utilize historical test execution data for improving the test process within a Quality Assurance (QA) department. The thesis research introduces two custom-built JavaScript algorithms, createTestRun and updateTestRun, designed to optimize the selection of test cases and effectively reduce redundancy in between test runs. The algorithms were developed by the thesis authors and operate on data retrieved from an existing test management API, where the createTestRun algorithm automatically creates test runs and the updateTestRun algorithm updates an existing test run based on the execution history of other test runs. The data collected are filtered and prioritized based on when they were previously executed or approved. After evaluation of the developed algorithms, it is concluded that the research demonstrates how targeted test selection and filtering historical data can significantly reduce the test scope. The result shows a promising growth in time savings, allocation of resources and offering a scalable solution to large-scale test runs.}},
  author       = {{Malmborg, Johanna and Niklasson, Ellen}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Optimizing Quality Assurance Process through Test Data Analysis}},
  year         = {{2025}},
}