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Course Details

Course Commencement
August 2023 (Tentative)
Course Completion
January 2024 (Tentative)
Language
English
Exam Dates
Multiple slots in January 2024 (Tentative)
Credits & LTP
Each course offers 5 credit points.
Lecture
3
Tutorial
1
Practical (Hands-on)
1
Credit
5
Recommended For:
Graduation Programme - B.E./B.Tech
Branch - Computer Science and Information Technology
Semester -
7 th  and  8 th

Course Summary

In today's era of digital disruption, business enterprises demand quicker time to market, lower cost of ownership, higher levels of quality and increased team productivity. This makes test data and test environment management critical components in meeting business objectives at large.

According to Gartner, "Digital business is driving a faster pace of delivery. Traditional testing teams can neither meet this pace, nor the expanded view of quality required". To bring in this faster pace of software delivery, the test data and environment become critical elements of the testing ecosystem and need to be managed well. The market size for Artificial Intelligence (AI) is expected to reach more than US$125 billion by 2025. The usage of AI/ML techniques is essential to enable testing ecosystems to conquer new frontiers in the digital world, which are increasing every day.

This course helps the students to understand the fundamentals as well as the industry trends in Test Data Management (TDM) and Test Environment Management (TEM) and introduces them to the concepts of building a smart Testing Ecosystem with the help of AI and Data Science. It helps students to develop a fair understanding of the various tools used in building a Testing Ecosystem and focusses on the range of skills that a test engineer needs to become successful.

With the increasing adoption of Agile and DevOps practices, creation and maintenance of complex data sets in different test environments has become a challenge, paving the way to a great career landscape for TDM and TEM engineers.

Hands-On

The course provides a hands-on session on test data and environment management tools. It also covers testing with Python and Machine Learning packages. Some of the key tools used in the practice sessions include JUnit, SonarQube, Selenium, and JMeter.

Students will have to leverage this environment to complete the industry assignment as well as to complete the Part B section of the summative assessment.

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RECOMMENDED PRIOR KNOWLEDGE
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Course Syllabus

The course syllabus will be delivered through a combination of eLearning resources, digital lectures, community based digital classrooms as applicable.
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RELATED COURSES
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Course Components

Digital Learning
Resources
Digital course accessible anytime and anywhere View Sample
Digital
Lectures
Digital lectures delivered by industry and academic experts View Sample
Academic Connect
Community
Focussed on building conceptual clarity View Sample
Industry Connect
Community
Focussed on building industry oriented applied knowledge View Sample
Industry
Assignment
Access to two industry related mini projects View Sample
Practice
Assessment
Access to two practice assessments for self-evaluation View Sample
Summative
Assessment
Test of theoretical and applied knowledge View Sample
Verifiable Digital
Certificate
Verifiable certificate on successful completion View Sample
Job
Opportunity
Visibility to job vacancies with leading corporate recruiters, subject to vacancies and their hiring policies View Sample

Testimonials

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