Software Reliability Course | IIT (ISM) Dhanbad | Prof. Subhashis Chatterjee
Course Details
| Exam Registration | 136 |
|---|---|
| Course Status | Ongoing |
| Course Type | Elective |
| Language | English |
| Duration | 12 weeks |
| Categories | Mathematics |
| Credit Points | 3 |
| Level | Undergraduate/Postgraduate |
| Start Date | 19 Jan 2026 |
| End Date | 10 Apr 2026 |
| Enrollment Ends | 02 Feb 2026 |
| Exam Registration Ends | 20 Feb 2026 |
| Exam Date | 25 Apr 2026 IST |
| NCrF Level | 4.5 — 8.0 |
Mastering Software Reliability: A 12-Week Journey with an Industry Expert
In today's digital world, where software powers everything from banking to healthcare, the reliability of these systems is non-negotiable. A single bug or system failure can have catastrophic consequences. How do engineers build software that users can truly depend on? The answer lies in the specialized field of Software Reliability.
We are excited to introduce a comprehensive 12-week course designed to demystify this critical domain. Guided by Prof. Subhashis Chatterjee from IIT (ISM) Dhanbad, this course blends rigorous theory with practical applications, preparing you to design and maintain robust, fault-tolerant software systems.
Meet Your Instructor: Prof. Subhashis Chatterjee
Learning from an expert with both deep academic roots and extensive practical experience is invaluable. Prof. Chatterjee brings precisely that to this course.
- Academic Credentials: He holds an M.Sc. and Ph.D. from the prestigious IIT Kharagpur.
- Teaching Experience: With over a decade at IIT (ISM) Dhanbad and prior experience at Sikkim Manipal Institute of Technology, he has mastered the art of explaining complex concepts. He has extensively handled core courses like Software Engineering, Data Structures, Machine Learning, and, of course, Software Reliability.
- Research & Industry Projects: His expertise is backed by research and development projects funded by agencies like UGC and ISRO in areas directly related to this course: Software Reliability and Dependability Analysis for real-life systems.
- Student Guidance: He has successfully guided numerous UG and PG students through dissertations in Software Reliability, Dependability Analysis, and the application of ML/DL in these fields.
About the Course: What You Will Learn
This course is a deep dive into the principles and practices that ensure software systems perform correctly under specified conditions for a defined period. It moves beyond basic testing to explore predictive modeling and quantitative assessment.
Course Objective: To equip you with the essential knowledge to model, predict, and enhance the reliability of software systems, using both traditional statistical methods and modern data-driven, AI-based approaches.
Intended Audience: This course is perfectly suited for:
- Undergraduate and Postgraduate students of CSE, ECE, Mathematics & Computing, Data Science, and related fields.
- Software Developers, Engineers, and Quality Assurance Professionals looking to formalize and advance their understanding of system robustness.
- Researchers and PhD scholars interested in the quantitative aspects of software quality.
Prerequisites: A basic understanding of probability and statistics is helpful but not mandatory. The course begins with foundational concepts to ensure all participants can follow along.
Industry Support: The skills taught are directly applicable in any organization involved in software development, maintenance, or quality assurance—from tech giants to innovative startups. Understanding reliability is key to building trust in software-driven products.
Detailed 12-Week Course Layout
The course is meticulously structured to take you from fundamentals to advanced applications.
| Week | Topics Covered |
|---|---|
| Week 1 | Introduction to probability concepts and software engineering fundamentals. |
| Week 2 | Detailed exploration of core reliability theory. |
| Week 3 | Classification and understanding of various software reliability models. |
| Week 4 | Fault dependency and the role of testing effort in reliability. |
| Week 5 | The debugging process and managing change point issues. |
| Week 6 | Software release time analysis and designing fault-tolerant systems. |
| Week 7 | Introduction to Soft Computing techniques for reliability. |
| Week 8 | Applying regression and classification models to reliability problems. |
| Week 9 | Data-driven approaches in software reliability assessment. |
| Week 10 | Broader software dependability issues: availability, safety, maintainability. |
| Week 11 | Hands-on session: Introduction to MATLAB/statistical software for reliability analysis. |
| Week 12 | Cutting-edge AI and Machine Learning applications in reliability analysis. |
Key Textbooks and References
To supplement the lectures, the course draws from seminal texts in the field, including:
- Musa, Iannino, & Okumoto: Software Reliability Measurement, Prediction and Application (McGraw-Hill, 1987).
- Kapur, Pham, Gupta, & Jha: Software Reliability Assessment with OR Applications (Springer, 2011).
- Yamada, S.: Software Reliability Modeling: Fundamentals and Applications (Springer, 2014).
- Pham, Hoang: System Software Reliability (Springer, 2006).
- Shooman, Martin L.: Software Engineering Design, Reliability and Management (McGraw-Hill, 1983).
Why Enroll in This Software Reliability Course?
This course is more than an academic module; it's career investment. In an era where software failures make headlines, professionals who can architect for reliability are in high demand. You will gain:
- Theoretical Foundation: A strong grasp of reliability models and metrics.
- Practical Skills: Hands-on experience with tools and techniques for reliability prediction and analysis.
- Future-Proof Knowledge: Exposure to how AI and Machine Learning are revolutionizing the field.
- Industry Relevance: Concepts directly applicable to roles in development, testing, DevOps, and system architecture.
Join Prof. Subhashis Chatterjee on this 12-week journey to master the art and science of building software that doesn't just work, but works reliably, consistently, and securely. Enroll today and take a significant step towards becoming an architect of dependable digital futures.
Enroll Now →