Automatic test code generation from acceptance test cases for large-scale software products
Typ
Examensarbete för masterexamen
Program
Computer systems and networks (MPCSN), MSc
Publicerad
2022
Författare
Jukanti, Kuvalaya Datta
Kumar, Prashant
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
The process of evaluating whether or not a software system is conformed to the
requirement specifications plays an important role in a large-scale software environment.
The test engineers identify the test scenarios and implement the code according
to the software standards which enables the verification of required criteria
for the product delivery. In the development model adopted by several companies,
including Ericsson, the requirements for a product are defined first on a high level.
These requirements are analysed and transformed into test plans or test cases which
are tested after the product development stage. But today, Ericsson has adopted a
new practice of software development which is the test-driven development process
where the test cases are defined before the software is fully developed. The test
engineers then write executable code for the test cases and test the product for different
scenarios.
However, in the context of large-scale software development with multiple products
and teams, it is not trivial to implement code for all the test cases. Today, the
common way of performing acceptance testing is through manual labour by understanding
the requirements, implementing the test scripts and execute them. The
developers create tests to determine if the requirements are met with the contract.
This serves as one of the challenges as it is a time consuming process. Thereby,
it is important to automate the testing process to rely less on the test engineers
and sufficiently serve the purpose. Today there are more advanced technologies and
resources which could help in developing such a tool that can reduce the costs and
time for the company.
This project has as its goal to automate the process of translating the test description
to test code. The approach involves the use of natural language processing
techniques and other deep learning techniques for analyzing the the test case specification
written in natural language and generate the corresponding executable test
code. This test code follows the syntax of Robot Test Automation Framework.
However, as every test scenario involves different parameters to consider, the aim
is to generate the code with suitable functions and be user-friendly allowing human
experts’ adjustments to add configurations and parameters.
Beskrivning
Ämne/nyckelord
Acceptance testing , Test case specifications , Automatic test code generation , Natural Language Processing , Deep Learning , Robot Framework