Lecture 26 of 60Week 6
Lecture 26 : Random Environmental Effect Model
Prof. Subhashis Chatterjee · IIT-ISM Dhanbad
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All the lectures
- Lecture 01 : Introduction to Probability
- Lecture 02 : Probability Distributions
- Lecture 03 : Probability Distributions & Stochastic Process
- Lecture 04 : Parameter Estimation & Software Engineering
- Lecture 05 : Software life cycle Model & Concepts of Software Engineering
- Lecture 6 : Moduler Design
- Lecture 7 : Reliability Theory and its Measures.
- Lecture 8 : Reliability measures and System Confugaration.
- Lecture 9 : Redundancy in Reliabity
- Lecture 10 :Introduction to Software Reliability
- Lecture 11 : Software Reliability Measures & Reliability Model Classifications
- Lecture 12 : Time between Failure Model
- Lecture 13 : Fault Seeding Modela nd Input Domain Based Model
- Lecture 14 : Fault Count Models
- Lecture 15 : Markov Models
- Lecture 16 : Different Parameters of Musa Execution Time Model and Their Uses
- Lecture 17 : Learning Parameter-based Models
- Lecture 18 : Models with Fault Dependency and Debugging Time-lag
- Lecture 19 : Models with Testing Effort Function
- Lecture 20 : Models with Fault Detection, Correction, and Imperfect Debugging Process
- Lecture 21 : Important Imperfect Debugging Models
- Lecture 22 : Test Coverage Model
- Lecture 23 : Discrete Software Reliability Growth Models
- Lecture 24 : Change Point-Based Reliability Models
- Lecture 25 : Stochastic Software Reliability Models
- Lecture 26 : Random Environmental Effect Model
- Lecture 27 : Multi Release Software System
- Lecture 28 : Software Scheduling
- Lecture 29 : Software Release Time Analysis
- Lecture 30 : Software Reliability In N Version Programming
- Lecture 31 : Time Series based Software Reliability Modesl
- Lecture 32 : Important Time Series Based SRGMs
- Lecture 33 : Introduction to Fuzzy Set Theory
- Lecture 34 : Fuzzy Inference Theorem
- Lecture 35 : Soft Computing-Based Optimization Techniques
- Lecture 36 : Introduction to Machine Learning Techniques
- Lecture 37 : Some Supervised Machine Learning Models
- Lecture 38 : Deep Learning Techniques
- Lecture 39 : Some Classification Techniques
- Lecture 40 : Unsupervised Machine Learning Models
- Lecture 41 : Neural Network and Deep Learning-based Reliability Models
- Lecture 42 : Early Phase Software Metrics
- Lecture 43 : Early Phase Fault Prediction
- Lecture 44 : Fault Prone Module Classification Models
- Lecture 45 : Deep Learning Models for Module Classification
- Lecture 46 : Hybrid Software Classification Models
- Lecture 47 : Imbalance Learning Models
- Lecture 48 : Transfer Learning-Based Models
- Lecture 49 : Unsupervised and Semi-Supervised Fault Prone Module Classification Models
- Lecture 50 : Software Dependability Analysis
- Lecture 51 : Reqirement Phase Dependability Model
- Lecture 52 : Design Phase Dependability Model
- Lecture 53 : Tyupe-2 Fuzzy-based Dependability Model
- Lecture 54 : Hands-On to Minitab
- Lecture 55 : Dependability Model and MATLAB Introduction
- Lecture 56 : Software Aging and Rejuvenation
- Lecture 57 : Some Aging Models and Software Re-engineering
- Lecture 58 : Hands On Python Programming with Libraries
- Lecture 59 : Regression and Classification Models (Hands On)
- Lecture 60 : Hands-On With Some Machine Learning Models