About this previous year paper
SOFTWARE RELIABILITY (EIT063)
Section-wise Solved Answers & Notes
SECTION – A (10 × 2 = 20 Marks)
Very short & direct answers
(a) Software vs Hardware Reliability
| Software Reliability | Hardware Reliability |
|---|---|
| Failure due to design defects | Failure due to wear & tear |
| Does not degrade physically | Degrades with time |
| Improved by debugging | Improved by replacement |
(b) Defect rate and reliability
• Defect rate: Number of defects per unit size (KLOC / FP).
• Reliability: Probability that software performs failure-free for a specified time.
(c) Tools of software reliability techniques
• Failure data analysis tools • Curve-fitting tools
• Reliability modeling tools (SARA, CASRE) • Statistical analysis tools
(d) Two matrices to define software reliability
• Defect density • Mean Time To Failure (MTTF)
(e) Curve fitting in software reliability
Curve fitting is the process of matching collected failure data with a mathematical reliability model to predict future failures.
(f) Defect, fault and failure • Defect: Error in code or design
• Fault: Incorrect internal state • Failure: Incorrect external behavior
(g) Accuracy of estimating project schedule
Accuracy is generally low in early phases and improves as project progresses; early estimates may vary by ±25–50%.
(h) Software reliability model
A mathematical model that predicts software failure behavior over time based on historical failure data.
(i) Parametric reliability growth model
A model that assumes specific parameters to describe failure intensity reduction over time (e.g., exponential, logarithmic models).
(j) Need of documents and matrices
They help in:
• Tracking defects • Measuring quality
• Predicting reliability • Improving decision-making
SECTION – B (Attempt Any Five) (5 × 10 = 50 Marks)
(a) Software metrics for analysis and design models
Metrics used:
• Size metrics: LOC, Function Points • Complexity metrics: Cyclomatic complexity
• Design metrics: Coupling, cohesion
They help evaluate maintainability, reliability, and testability early.
(b) Software Quality Assessment Models
These models evaluate quality using metrics and attributes.
Examples: • McCall’s Quality Model
• Boehm’s Quality Model
(Block diagram: Requirements → Metrics → Analysis → Quality rating)
(c) Size & structure in measuring product attributes
• Size: LOC, FP → indicates effort & defects
• Structure: Coupling, cohesion → indicates maintainability
Product quality is ascertained using defect density, reliability, and maintainability metrics.
(d) Evolution of SQA & major issues
Evolution: • Testing-oriented → Process-oriented → Quality management
Major SQA issues:
• Incomplete requirements • Poor testing
• Schedule pressure • Inadequate metrics
(e) Predicting reliability techniques (example)
Techniques include: • Failure rate modeling
• Reliability growth models • Statistical estimation
Example: Predicting remaining failures using exponential model after test phase.
(f) Hierarchical model of software quality assessment
Quality factors → Quality criteria → Metrics
Example: Reliability → Failure rate → MTTF
This structure provides clear traceability from metrics to quality goals.
(g) Zero defect software & reliability attributes
Zero defect software: Aim to prevent defects rather than fix later.
Reliability attributes:
• Failure rate • MTTF
• Availability • Recoverability
(h) Fault & failure data collection & phase-based defect removal
• Defects are collected phase-wise (requirements, design, code, testing).
• Early removal reduces cost.
Pattern:
Maximum defects introduced in coding, ideally removed in reviews/testing.
SECTION – C (Attempt Any Two) (2 × 15 = 30 Marks)
Q3 (a) Logarithmic Poisson Execution Time Model
Failure intensity:
λ(μ)=λ0e−θμ\lambda(\mu) = \lambda_0 e^{-\theta \mu}λ(μ)=λ0e−θμ
Given:
Decay parameter θ = 0.25/failure
Resources required include:
• Testing time • Skilled testers
• Debugging effort • Tools and infrastructure
As failures are removed, failure intensity decreases logarithmically.
Q3 (b) Error seeding, failure rate & curve fitting
• Error seeding: Artificial defects inserted to estimate remaining defects
• Failure rate: Failures per unit time
• Curve fitting: Matching real failure data with reliability model
Q4 (a) Software reliability technique (block diagram)
Process:
Requirements → Design → Coding → Testing → Failure data collection → Reliability modeling → Prediction
Purpose: Estimate and improve reliability before release.
Q4 (b) Static code metric
Static metrics measure code without execution.
Examples: • Cyclomatic complexity
• Lines of code • Comment density
Used to predict error-prone modules.
Q5 Short Notes (Any Three)
(a) Rayleigh model
Used when failure rate increases initially, then decreases; suitable for large projects.
(b) SARA tool
SARA (Software Automated Reliability Analysis) supports:
• Failure data analysis • Reliability prediction
• Model fitting
(c) Metrics for software maintenance
• MTTR • Change request rate
• Defect backlog
(d) Major SQA activities
• Reviews & audits • Testing
• Metrics collection • Process improvement