Leveraging AI for Predictive Technical Debt Management in SAP Development Ecosystems: Case Studies and Future Prospects
Keywords:
Technical debt, SAP, Technical Debt Management, AIAbstract
Technical debt (TD) acts as the silent killer in massive, integrated SAP ecosystems and is often the main reason projects crash and burn. We simply can’t afford to be reactive anymore; we need to get ahead of the problem with Predictive Technical Debt Management (PTDM). This paper proposes a PTDM framework that uses Artificial Intelligence (AI) to handle three critical jobs: predicting what will break, prioritizing what to fix, and keeping the deployment line moving. We use a binary classification model (Algorithm 1) to guess the odds of an ABAP object failing, and we apply Natural Language Processing (NLP) to support tickets to figure out which bugs are actually hurting the business (Algorithm 2). By wrapping this in a Continuous PTDM Loop (Algorithm 3), we automate the creation of remediation tasks. Our operational case studies like an S/4HANA migration triage and continuous performance forecasting (Algorithm 4) show that this AI-driven approach speeds up custom code cleanup and stabilizes the system by calculating the "interest rate" of debt before it becomes too expensive to pay off. We wrap up by discussing future research into Deep Learning for semantic debt detection and managing debt in cloud-native SAP landscapes.
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