Searcharxiv⌕ Search

arXiv subjects

Nemania Borovits

Publications and source records attributed to Nemania Borovits.

2 recordsLinked to original sources
arXiv2026

Privacy Engineering: A Systematic Literature Review

Privacy obligations under GDPR increasingly shape software engineering. We synthesize 90 studies from 2018 to 2025 using a systematic review with thematic synthesis to chart privacy engineering. Thirteen dimensions form two recurrent cores: Privacy Enhancing Technologies (PETs) with Privacy Metrics (PM) and Verification and Testing (VT) and Governance and Accountability (GA) with Transparency and Communication (TC) and Organizational Measures (OM). Modeling and Specification (MS) mediates between the cores. Lifecycle mapping shows concentrations at requirements and design (MS, GA), at implementation and verification (PETs, VT, PM, TC) and at operation and decommissioning (GA, OM, Data Subject Rights Management (DSRM), Incident Response and Management (IRM), Lifelong Management (LM)). Handoffs link models to rules and tests, mechanisms to metrics and deployments such as enclaves and ledgers to governance records. Domains reweight but do not alter structure: healthcare weights GA with VT and PETs, IoT and edge weight PETs with VT and PM at device and edge, web measurement weights TC with VT, AI and ML weight PETs with PM. IRM, LM and Data Minimization and Purpose Limitation (DMPL) are less often primary foci, signaling priorities for future work. The results provide a practical map and a replication-ready scaffold for assessment and updates.

cs.SE↗
arXiv2020

DeepIaC: Deep Learning-Based Linguistic Anti-pattern Detection in IaC

Linguistic anti-patterns are recurring poor practices concerning inconsistencies among the naming, documentation, and implementation of an entity. They impede readability, understandability, and maintainability of source code. This paper attempts to detect linguistic anti-patterns in infrastructure as code (IaC) scripts used to provision and manage computing environments. In particular, we consider inconsistencies between the logic/body of IaC code units and their names. To this end, we propose a novel automated approach that employs word embeddings and deep learning techniques. We build and use the abstract syntax tree of IaC code units to create their code embedments. Our experiments with a dataset systematically extracted from open source repositories show that our approach yields an accuracy between0.785and0.915in detecting inconsistencies

cs.SE↗