Master of Science (Software Engineering)

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Program Info (  Eligibility Criteria  )

The MS Software Engineering curriculum has been designed to give the students a good understanding of best software engineering methodologies and practices, emerging technologies, and their application in various industries. The goal of the program is to teach students to become leaders in engineering high quality computing solutions to solve real life problems by conducting high quality applied research.

Recommended Courses

The following core courses are recommended to be completed before entering the MS (SE) program.

  1. Computer Programming
  2. Software Engineering
  3. Software Quality Engineering
  4. Data Structures
  5. Object Oriented Analysis and Design
  6. Human Computer Interaction

A student selected for admission having deficiency in the above stated courses may be required to study a maximum of FOUR courses. These courses must be passed in the first two semesters. Deficiency courses shall be determined by the Graduate Studies Committee, before admitting the student. No fee is charged for studying deficiency courses. A student cannot take MS courses unless all specified deficiency courses have been passed.

Typical course load in a semester is four courses. However, NUCES staff cannot register for more than two courses in a semester. For successful completion of the MS SE degree, the student must undertake a 6 credit hour MS Project, spread over two regular semesters.

Award of Degree

For the award of MS degree, a student must have:

  • Passed courses totalling at least 30 credit hours, including all those courses which have been specified as Core courses
  • Obtained a CGPA of at least 2.5
Tentative Study Plan
Sr. No Course Name Crdt Hrs.
Semester 1
1 Applied Programming 1 NC
2 Adv. Software Requirements Engineering 3+0
3 Adv. Quality Assurance 3+0
4 Computing Elective-I 3+0
Sr. No Course Name Crdt Hrs.
Semester 2
1 Adv. Software Architecture 3+0
2 Computing Elective-II 3+0
3 Research Methodology 3+0
Sr. No Course Name Crdt Hrs.
Semester 3
1 Computing Elective-III 3+0
2 MS Thesis-I/MS Project-I 0+3
Sr. No Course Name Crdt Hrs.
Semester 4
1 Computing Elective-IV 3+0
2 MS Thesis-II/MS Project-II 0+3

Note 1: Applied Programming course is of No Credit (NC), but it must be passed.

Note 2: Registration in “MS Thesis - I” is allowed provided the student has:

  • Earned at least 15 credits
  • Passed the “Research Methodology” course
  • CGPA is equal to or more than 2.5

Program Educational Objectives (PEO)

  1. Prepare students who can critically apply concepts, theories, and practices to provide creative solutions to complex computing problems.
  2. Prepare students to effectively communicate their ideas in written and electronic form and prepare them to work collaboratively in a team environment.
  3. Prepare students with theoretical background of software engineering concepts, and train them on applied research of the field, needed to secure a doctorate position in the future.
  4. Prepare students to join a dynamic and diverse career position in a computing-related field, and to maintain a growing career in a rapidly evolving field.
  5. Prepare students who can define, plan, implement, and test a medium-sized software project using appropriate software engineering processes, methods, and techniques.
  6. Theories and practices to provide creative solutions to complex computing problems.
  7. To respond to the current and emerging industrial needs utilizing modern trends for building complex software systems.

Program Learning Outcomes (PLOs)

1

Knowledge of Data Science

Have an advanced, and coherent disciplinary and interdisciplinary knowledge of Data Science technologies, and research principles and methods.

2

Critical Thinking, Design Thinking and Decision-Making Skills

Develop problem solving, design and decision-making skills to identify and provide innovative solutions to complex Data problems through application of related technologies and techniques.

3

Ethics and Social Responsibility

Demonstrate mindfulness of professional practices in a global and sustainable context and act with professional accountability and integrity.

4

Research Methods Competence

Apply knowledge of research principles and methods to plan and execute a research-based practical project with personal autonomy and accountability.

5

Communication Skills

Interpret, document and present the core issues, problem statements, evaluation reviews, requirements and findings in developing Data Science research work.