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Supervisor: Dr. Syed Mubashir Ali

 

Identification, Analysis and Mitigation of Risks Factors in Global Software Engineering – A hybrid Multi-Criteria Decision Making and Machine Learning based approach

 

Supervisor: Dr. Syed Mubashir Ali/ Associate Professor

Campus/School/Dept: BULC/BSEAS/CS

Research Areas: Global Software Engineering, Decision Support System, Multi Criteria Decision Making, Supply Chain Management

Supervisory Record:

  • PhD Produced: 2
  • PhD Enrolled:0
  • MS/MPhil Produced:0
  • MS/MPhil Enrolled:0

 

Topic Brief Description:

Global software development (GSD) has gained wider adoption since Covid 19. It has become a new norm now that team members are located at geographically dispersed location to deliver a common project. The reason for widespread adoption of GSD is travelling restrictions on some countries, lack of available talent locally, availability of cheaper resource, wars and political instability among other factors. This research proposal aims to first identify the risks inherent to GSD, later analyze and rank those risks and in the end use Machine Learning or Intelligent Systems to find ways of mitigating those risks. This research will be significant not only locally for Pakistan but also globally for various software houses and technology firms as they would be better able to deliver their software projects using this cutting-edge research.

 

Research Objectives/Deliverables:

  1. Identify various risk factors associated with GSD both generally as well as specific to Pakistan or emerging economies’ perspective using literature and Delphi techniques.
  2. Analyze the identified risks using various multi-criteria decision-making techniques in order to prioritize those risks.
  3. Devise strategies for mitigating GSD risks using Machine learning algorithms or intelligent systems.

Research Questions:

  1. What are the risks related to GSD?
  2. What are the most pertinent risk factors related to GSD and specific to emerging economies such as Pakistan?
  3. How can GSD risks be mitigated using ML or Intelligent Systems.

 

Candidate’s Eligibility Profile:

  1. The applicant must have an MS/MPhil/Equivalent degree in CS/SE/IT with CGPA >0. Besides, applicants must have a background or basic understanding of mathematics, statistics, machine learning and related fields.
  2. Experience with programming languages such as MATLAB, Python or C/C++, is advantageous. Candidates should thrive in an international environment and have excellent communication skills to actively contribute to team research efforts.
  3. Proficiency in spoken and written English is essential. We value independence and responsibility while promoting teamwork and collaboration among colleagues.