Software Architecture Optimization for Mission-Critical ISR Systems Using Adaptive Code Refactoring Models (Published)
Software architecture integrity is a foundational determinant of operational reliability in Intelligence, Surveillance, and Reconnaissance systems, yet the structural decay that accumulates across long deployment lifecycles represents one of the most consequential and least visible threats to sustained mission capability. Existing approaches to architectural quality management in defense software environments remain predominantly reactive, engaging remediation resources only after structural deficiencies have manifested as operational anomalies — a posture that forfeits the intervention lead time necessary to prevent mission impact and compounds remediation cost through deferred action. This paper proposes a novel predictive software architecture evaluation model that employs adaptive, context-aware code refactoring strategies to detect and remediate architectural decay in mission-critical ISR systems before structural deficiencies precipitate operational failures. The model is grounded in real-world architectural evolution data derived from AIMS-ISR, a representative long-lifecycle ISR processing platform, and is designed to operate within the stringent safety classification, certification, and change-control constraints that characterize fielded defense software environments. The framework integrates a six-dimensional architectural health vector — encompassing coupling, cohesion, cyclomatic complexity, response time variance, resource utilization patterns, and dependency propagation cost — within a Long Short-Term Memory predictive engine and an operationally context-sensitive refactoring decision module, validated through stratified temporal cross-validation against a 671-event multi-system architectural evolution corpus. Empirical evaluation against the AIMS-ISR baseline demonstrates that the predictive model achieves a macro-averaged F1 score of 0.913, with an area under the ROC curve of 0.978 for the critical decay class, delivering an average advance warning horizon of 5.3 release cycles prior to confirmed architecture-attributable operational anomalies. Application of the engine’s refactoring recommendations produced a 42.7% mean reduction in composite architectural debt indicators, a 49.7% reduction in real-time response time variance, and a 76.3% reduction in memory utilization growth rate across treated events, at a remediation cost ratio of 4:1 relative to post-failure corrective effort. Comparative evaluation confirms that the adaptive model outperforms reactive, static rule-based, and random refactoring baselines across all reported dimensions by statistically significant margins. These findings establish predictive architectural stewardship as a technically rigorous and operationally viable paradigm for sustaining the reliability and mission assurance of ISR systems and, by extension, the broader class of long-lifecycle, safety-critical software platforms on which modern defense and aerospace operations depend.
Keywords: Adaptive Software Engineering, Architectural Decay Detection, Intelligence Surveillance and Reconnaissance (ISR), Long Short-Term Memory (LSTM) Networks, Mission Assurance, Mission-Critical Systems, Predictive Code Refactoring, Real-Time Systems Reliability, Software Architecture Evaluation, Technical Debt Management