Software Configuration Management Quiz

Software Evolution and Maintenance

• Software evolution is the continuous change and improvement of software systems to adapt to new requirements and operational environments.
• This involves adding features, enhancing performance, or porting software to new platforms.
• Software maintenance is the process of ensuring the software operates as intended after deployment.
• This includes tasks like bug fixes, adaptations, optimizations, and maintaining system stability.
• Proper evolution and maintenance ensure software remains relevant, cost-effective, and efficient.

Maintenance vs. Evolution

• Maintenance focuses on fixing defects and ensuring continued operation without significant architecture changes.
• It's reactive, addressing issues like bugs and outdated components.
• Evolution incorporates broader, planned changes to adapt, improve and modernize the software.
• It's proactive, adding features, improving performance, and modernizing platforms.
• Examples include bug fixes in photo editing software, and introducing AI-based object removal.
• Maintenance emphasizes stability, while evolution focuses on growth and improvement.
• Maintenance activities are corrective and adaptive, while evolution activities are strategic and large-scale.

Types of Maintenance

• Corrective Maintenance: Fixing problems like bugs or security vulnerabilities.
• Adaptive Maintenance: Adjusting the software to work in new environments, like new hardware or operating systems.
• Perfective Maintenance: Enhancing the function or performance of the software based on user feedback.
• Preventive Maintenance: Identifying and mitigating potential issues to reduce future problems.

Examples of Maintenance types

• Corrective: Fixing a login vulnerability in an e-commerce website.
• Adaptive: Updating a web app to operate with new browser versions.
• Perfective: Improving loading speed of an online shopping website.
• Preventive: Refactoring legacy code in a mobile app.

Lehman's 8 Laws of Software Evolution

• Continuing Change: Software needs continual evolution to remain relevant.
• Increasing Complexity: Systems become more intricate over time without intervention.
• Self-Regulation: Evolutionary changes follow patterns.
• Conservation of Organizational Stability: Resources for evolution tend to stabilize over time.
• Conservation of Familiarity: Significant changes need consensus among developers and stakeholders.
• Continuing Growth: Functionality increases to meet user needs.
• Declining Quality: Software quality degrades without upgrades.
• Feedback System: Evolution is influenced by interacting feedback loops.

Software Evolution Models

• Iterative Models: Emphasize gradual requirement emergence and incremental system building, informed by feedback loops like Agile development.
• Staged Models (e.g., Rajlich and Bennett's): This model has distinct stages: initial development, evolution, servicing, and phaseout, focusing on maintaining the system instead of adding new features at certain points.
• Change Mini-Cycle Model (Yau): This model is a cyclical approach with five phases: Change Request, Analyze and Plan Change, Implement Change, Verify and Validate, and Documentation Change to address ongoing updates and bug fixes.
• This is a repetitive cycle that facilitates handling the needs of software updates and bug fixes.

Phases in Software Maintenance

• Understanding the Code: Thoroughly examining existing code and documentation to identify areas that need change.
• Modifying the Code: Implementing necessary changes to address issues, add new features, or optimize existing code.
• Revalidating the Code: Verifying the changes to ensure the software functions correctly and maintains stability.

Software Maintenance Standards

• ISO/IEC 14764: A standard that provides guidelines and procedures for managing software maintenance, emphasizing the need for systematic processes.
• IEEE/EIA 1219: A structured standard for managing maintenance activities, covering phases such as problem identification, analysis, design, implementation, and testing. This highlights the need for documentation and using metrics to track the effectiveness of maintenance efforts.

Configuration Management (SCM)

• Configuration Management (SCM) is crucial for tracking and managing software changes throughout the lifecycle.
• SCM helps with identifying software configurations through defining and categorizing all software artifacts.
• SCM is also important from a control point of view, as it ensures that modifications are approved, tracked, and applied systematically. This reduces uncontrolled changes that can introduce errors or inconsistencies.

Auditing and Tracking Software Configurations

• SCM systems audit the current state of the software to maintain compliance with specifications. They document all changes for traceability.
• Tracking configures status involves recording changes (when, why, by whom), which helps maintain a history trail.

Challenges in Software Evolution and Maintenance

• Managing Complexity: As software evolves, its complexity often increases, potentially hindering further evolution and making maintenance difficult without proper management.
• Skills and Knowledge Requirements: Maintaining and evolving systems often requires specialized knowledge of the existing system and any advanced or new technologies being implemented.
• Cost and Resource Allocation: Software maintenance can be expensive and resource-intensive, especially when frequent updates and large systems are involved .
• Risk of System Obsolescence: In the absence of updates, systems may become obsolete and incompatible with modern technologies, raising the risks associated with not updating and maintaining the software.
• Addressing the ongoing challenge of ensuring the software stays up-to-date and meets prevailing user needs is important.

Future Trends in Software Evolution

• Automation: Automation plays a crucial role by handling repetitive tasks, reducing human intervention, and improving speed and accuracy.
• AI Integration: Artificial intelligence (AI), particularly machine learning and natural language processing (NLP), can greatly assist in code analysis, bug detection, and predicting maintenance needs. This allows for proactive maintenance and improvement of software systems.
• Self-adaptive Software: Adaptive systems can monitor and modify themselves based on real-time data and changing needs without human intervention. Self-adaptive solutions are an important component in future software evolution; these systems are capable of evolution and adaptation without human intervention.
• Effective feedback loops are vital, allowing systems to adapt throughout the entire software development lifecycle. Multi level feedback will enable ongoing and effective evolution, in response to user inputs, environmental changes, or system performance.