Module Code: H9EEAI
Long Title Engineering and Evaluating Artificial Intelligence Systems
Title Engineering and Evaluating Artificial Intelligence Systems
Module Level: LEVEL 9
EQF Level: 7
EHEA Level: Second Cycle
Credits: 5
Module Coordinator: Rejwanul Haque
Module Author: Shauni Hegarty
Departments: School of Computing
Specifications of the qualifications and experience required of staff  
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Comprehend, contrast, assess, and apply software architecture principles in the design of AI systems.
LO2 Theoretically evaluate the AI systems in terms of completeness, complexity, and admissibility.
LO3 Evaluate, summarise, critique, and present the quality and performance of AI systems.
LO4 Determine and critique the infrastructure for the deployment of AI systems.
Module Recommendations

This is prior learning (or a practical skill) that is required before enrolment on this module. While the prior learning is expressed as named NCI module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).

No recommendations listed
Co-requisite Modules
No Co-requisite modules listed
Entry requirements  

Module Content & Assessment

Indicative Content
Artificial Intelligence Engineering
•Overview of AI Engineering•Software Development Life Cycle•Agile Development for AI
Software Architecture
Software Architecture•Architectural Structures and Views•What Makes a "Good" Architecture?•Importance of Software Architecture•Contexts of Software Architecture•Understanding Quality Attributes•Specifying Quality Attribute Requirements•Quality Attributes (i.e., Availability, Interoperability, Modifiability, Performance, Security, Testability, Usability, Deployability, Energy, and Safety)
Architectural Solutions
•Architectural Patterns•Architecting in the Cloud•Architecture for Machine Learning and Artificial Intelligence
Designing the Architecture
•Architecture in Agile Projects•Design Strategy•Attributed-Driven Design (ADD) Method•Steps of ADD
Documenting Architecture
•Notation for Architecture Documentation•Views•Documenting Behaviour•Architecture Documentation and Quality Attributes•Documenting in Agile Projects
Architecture Evaluation
•Evaluation Factors•The Architecture Trade-off Analysis Method•Lightweight Architecture Evaluation
Computational Complexity
•Basic Conventions•Big O Notation•Deterministic and Non-Deterministic Models of Computation•Class P, NP and NP Complete•Coping with NP Hardness•Time and Space Complexity
Evaluating Algorithms
•Admissibility of a Heuristic•Completeness•Evaluating Algorithm Correctness•Sensitivity Analysis
Software Quality Assurance
•Software Quality Characteristics•Software Testing Life Cycle•Types of Testing Techniques (e.g., Black-Box Testing, White-Box Testing)
Deployment Pipeline
•Introduction to DevOps•Building and Testing•Deployment Strategies
Infrastructure Design
•Infrastructure Design, Testing, and Reuse•Modularity and Infrastructure Churn•Scalability•Distributed System
Assessment Breakdown%
End of Module Assessment50.00%


Full Time

Assessment Type: Formative Assessment % of total: Non-Marked
Assessment Date: n/a Outcome addressed: 1,2,3,4
Non-Marked: Yes
Assessment Description:
Formative assessment will be provided on the in-class individual or group activities. Feedback will be provided in written or oral format, or on-line through Moodle. In addition, in class discussions will be undertaken as part of the practical approach to learning.
Assessment Type: Continuous Assessment % of total: 50
Assessment Date: n/a Outcome addressed: 1,3
Non-Marked: No
Assessment Description:
This assessment will evaluate the learners’ comprehension of software architecture principles and skills for applying that knowledge to design AI systems. Learners will be provided with a description of an AI system or case study. Learners will be required to identify the minimum architectural and software quality requirements, design and document a software architecture for the proposed AI system.
End of Module Assessment
Assessment Type: Terminal Exam % of total: 50
Assessment Date: End-of-Semester Outcome addressed: 1,2,3,4
Non-Marked: No
Assessment Description:
The examination will be of two hours duration and may include a mix of: theoretical, applied and interpretation questions.
No Workplace Assessment
Reassessment Requirement
Repeat examination
Reassessment of this module will consist of a repeat examination. It is possible that there will also be a requirement to be reassessed in a coursework element.

NCIRL reserves the right to alter the nature and timings of assessment


Module Workload

Module Target Workload Hours 0 Hours
Workload: Full Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture Lectures 24 Per Semester 2.00
Independent Learning Independent Learning 89 Per Semester 7.42
Tutorial Tutorials 12 Per Semester 1.00
Total Weekly Contact Hours 3.00

Module Resources

Recommended Book Resources
  • Bass, L., Clements, P., & Kazman, R. (2022). Software Architecture in Practice(4th ed.). Addison-Wesley Professional. SEI Series in Software Engineering. [ISBN: 978-0136886099].
  • Bass, L., Weber, I., & Zhu, L. (2016). DevOps: A Software Architect’s Perspective. Addison-Wesley Professional. SEI Series in Software Engineering. [ISBN: 978-9332570375].
  • Mahfuz, A. S. (2016). Software Quality Assurance: Integrating Testing, Security, and Audit. Auerbach Publications. [ISBN: 978-1498735537].
Supplementary Book Resources
  • Arora, S. & Barak, B. (2009). Computational Complexity: A Modern Approach. Cambridge University Press. [ISBN: 978-0521424264].
  • Cervantes, H. & Kazman, R. (2016). Designing Software Architectures: A Practical Approach. Addison-Wesley Professional. [ISBN: 978-0134390789].
  • Hulten, G. (2018). Building Intelligent Systems: A Guide to Machine Learning Engineering. Apress. [ISBN: 978-1484234310].
  • Humble, J. & Farley, D. (2010). Continuous Delivery: Reliable Software Releases through Build, Test, and Deployment Automation. Addison-Wesley Professional.[ISBN: 978-0321601919].
  • Richards, M. & Ford, N. (2020). Fundamentals of Software Architecture: An Engineering Approach. O’Reilly. [ISBN: 978-1492043454].
  • Tarlinder, A. (2016). Developer Testing: Building Quality into Software. Addison-Wesley Professional.[ISBN: 978-0134291062].
This module does not have any article/paper resources
This module does not have any other resources
Discussion Note: