AI IN SOFTWARE QUALITY

Course Description

This course integrates AI4SE (AI for Software Engineering) and SE4AI (Software Engineering for AI Systems). Students learn how AI techniques improve software quality, including AI-based test generation, machine-learning defect prediction, and code-smell detection, together with AI-assisted development tools. The course then studies how to build, test, and maintain reliable AI-based systems, covering data and dataset quality, testing and debugging of ML models, fairness, bias and explainability, CI/CD and MLOps, and trustworthy and secure AI. Weekly laboratories use Python, Jupyter, scikit-learn, TensorFlow/Keras, and SonarQube. On completion, students understand the mutual relationship between AI and software engineering and can apply AI to software quality assurance.

AI IN SOFTWARE QUALITY

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Course outcomes

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Course Syllabus

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Reference Books & Course Materials

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Learning Outcomes

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Program Outcomes

1. Adequate knowledge in mathematics, science and engineering subjects pertaining to the relevant discipline; ability to use theoretical and applied knowledge in these areas in complex engineering problems.
2. Ability to identify, formulate, and solve complex engineering problems; ability to select and apply proper analysis and modelling methods for this purpose.
3. Ability to design a complex system, process, device or product under realistic constraints and conditions, in such a way as to meet the desired result; ability to apply modern design methods for this purpose.
4. Ability to devise, select, and use modern techniques and tools needed for analysing and solving complex problems encountered in engineering practice; ability to employ information technologies effectively.
5. Ability to design and conduct experiments, gather data, analyse and interpret results for investigating complex engineering problems or discipline specific research questions.
6. Ability to work efficiently in intra-disciplinary and multi-disciplinary teams; ability to work individually.
7. Ability to communicate effectively in Turkish, both orally and in writing; knowledge of a minimum of one foreign language; ability to write effective reports and comprehend written reports, prepare design and production reports, make effective presentations, and give and receive clear and intelligible instructions.
8. Recognition of the need for lifelong learning ; ability to access information, to follow developments in science and technology, and to continue to educate him/herself.
9. Consciousness to behave according to ethical principles and professional and ethical responsibility; knowledge on standards used in engineering practice.
10. Knowledge about business life practices such as project management, risk management, and change management; awareness in entrepreneurship, innovation; knowledge about sustainable development.
11. Knowledge about the global and social effects of engineering practices on health, environment, and safety, and contemporary issues of the century reflected into the field of engineering; awareness of the legal consequences of engineering solutions.

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