Static Code Analysis for Identification and Prioritization of Code Quality Remediation in a Resource-Constrained University Academic Information System

Muhammad Ridho Kurniawan Pratama (Information Systems and Technology, Faculty of Engineering, Universitas Negeri Jakarta, Jakarta, DKI Jakarta, Indonesia)
Deni Utama (Information Systems and Technology, Faculty of Engineering, Universitas Negeri Jakarta, Jakarta, DKI Jakarta, Indonesia)
Rauhil Fahmi (Information Systems and Technology, Faculty of Engineering, Universitas Negeri Jakarta, Jakarta, DKI Jakarta, Indonesia)
Ali Idrus (Information Systems and Technology, Faculty of Engineering, Universitas Negeri Jakarta, Jakarta, DKI Jakarta, Indonesia)

Teknika
Vol. 15 No. 1 (2026): March 2026

Software quality directly impacts organizational effectiveness, yet university information systems in developing nations frequently face compounded challenges, including resource constraints, limited developer expertise, and inadequate quality assurance mechanisms. Although static code analysis systematically detects software defects, translating technical findings into actionable, context-appropriate strategies remains problematic for resource-limited academic institutions. This research investigated structural quality, security vulnerabilities, and maintainability characteristics of a university academic information system, formulating contextually informed recommendations that reconcile technical assessment outcomes with institutional constraints. The case study employed four sequential phases: organizational context evaluation, quantification of code-complexity metrics, comprehensive quality assessment via static analysis, and development of context-sensitive recommendations. Correlation analysis examined relationships between structural complexity indicators and maintainability degradation. Examination of 127 PHP files revealed 9,628 quality defects, with maintainability issues accounting for 89.9%, alongside 18 critical security vulnerabilities and severe complexity, with individual methods reaching cyclomatic complexity values of 263. Strong positive correlations emerged between complexity metrics and maintainability problems (r = 0.938, p < 0.001), indicating that complexity is a reliable predictor of quality deterioration. Issue distribution patterns across all examined files suggested systemic quality degradation rather than isolated problematic modules. Findings document critical security exposures, excessive structural complexity, and pervasive maintainability deficiencies, validating significant associations between complexity and maintenance burden. The study proposes a bifurcated improvement approach encompassing tactical measures targeting immediate technical debt, including security vulnerabilities and architectural complexity, complemented by strategic interventions, providing academic information system managers with a prioritized remediation action calibrated to institutional resource constraints.