Software Architecture Recovery and High-Level Design Extraction

Lecture 10 of SE602 Software Re-Engineering addresses software architecture recovery, the reverse engineering discipline that reconstructs high-level design abstractions from low-level implementation artifacts. Over years of operational maintenance, software systems experience architectural drift and erosion, where implemented code diverges radically from original conceptual blueprints. Students examine automated clustering algorithms that group related source files, classes, and packages into cohesive subsystems based on shared data structures, naming conventions, and coupling metrics. The lecture covers reflexivity modeling using Software Reflexion Models, where engineers contrast a hypothesized high-level architecture with extracted source dependencies to expose architectural convergences, divergences, and absences across complex enterprise systems. Automated tool support, bytecode instrumentation, and static code analyzers are evaluated to detect architectural violations across repositories. Risk management frameworks, business case evaluations, and cost estimation models such as COCOMO II are analyzed for migration projects. Legal and ethical constraints surrounding reverse engineering, clean-room design implementations, and software intellectual property rights are reviewed. By engaging with solved past papers by Moaaz and Waqar Siddhu, learners gain actionable insights into recurrent university exam questions. Interactive quiz assessments on VU SIGMA facilitate rapid knowledge verification and retention of critical software re-engineering concepts. Comprehensive coverage of standard academic benchmarks ensures learners communicate with technical precision across software engineering disciplines. Detailed analysis of code smell topologies highlights the imperative of eradicating duplicate logic, long methods, and bloated god classes. Test harness construction including characterization testing and regression suites are contrasted with traditional unit testing strategies. Students evaluate the trade-offs between big-bang system rewrites and progressive evolutionary refactoring in mission-critical environments. The lesson explores cognitive program comprehension models, top-down hypothesis validation, and mental mappings of complex legacy architectures. Practical code illustrations demonstrate safe code transformations, automated refactoring preconditions, and behavioral equivalence preservation. Scholars analyze Chidamber-Kemerer object-oriented metrics including WMC, CBO, and RFC to quantify the architectural impact of refactoring. Cloud migration pathways including rehosting, replatforming, and cloud-native refactoring are examined as extensions of legacy modernization.