Hassan Reza

Mohamad Zamini, Hassan Reza, Minou Rabiei

Information extraction methods proved to be effective at triple extraction from structured or unstructured data. The organization of such triples in the form of (head entity, relation, tail entity) is called the construction of Knowledge Graphs (KGs). Most of the current knowledge graphs are incomplete. In order to use KGs in downstream tasks, it is desirable to predict missing links in KGs. Different approaches have been recently proposed for representation learning of KGs by embedding both entities and relations into a low-dimensional vector space aiming to predict unknown triples based on previously visited triples. According to how the triples will be treated independently or dependently, we divided the task of knowledge graph completion into conventional and graph neural network representation learning and we discuss them in more detail. In conventional approaches, each triple will be processed independently and in GNN-based approaches, triples also consider their local neighborhood. View Full-Text

Ismail Gargouri, Hassan Reza

Ensuring data quality in cloud-native Extract-Load-Transform (ELT) pipelines is increasingly challenging due to heterogeneous data sources, evolving schemas, and multi-backend execution environments. This paper presents a unified, multi-layer testing framework that integrates orchestration-level validation, declarative dbt tests, large language model (LLM)-generated semantic tests, and cross-store consistency checking between DuckDB and Snowflake, orchestrated through Apache Airflow. Controlled anomaly-injection experiments demonstrate that a manual-only baseline detected 7 of 16 injected anomalies. In contrast, both a manually expanded comparator and the proposed LLM-augmented configuration detected all 16, representing a 128.57% relative improvement in detection rate over the baseline. Post-migration cross-store validation confirmed exact agreement across all three curated tables. Of 25 LLM-generated test assertions, 9 were classified as useful, 4 as redundant, and 12 as executable but low-value. The complete workflow executed in 106.58 seconds across eight instrumented pipeline stages. These results demonstrate that LLM-driven semantic test synthesis can materially strengthen validation coverage while remaining operationally practical for production ELT environments.

Reid Anderson, Hassan Reza

The design of a system and its implementation are two tasks often carried out by different individuals on a development team, and can occur weeks or months apart. This creates a potential for divergence between real behavior and the designed model that an implementation is intended to match. Particularly as time passes and individuals who were present for the original conception of the design leave, a system can lose coherence and drift from intended design principles. Even with a robust system design, more is needed to ensure that the key implementation details match the design and that adherence to a particular strategy is not lost over time. This paper proposes an approach to address that concern for distributed systems using conformance checking, a methodology borrowed from process mining. Distributed traces produced by instrumented applications are evaluated for conformance by comparison to design traces. The resulting conformance percentage is a quantitative metric that can be tracked over time to determine how closely a concrete implementation corresponds to the key attributes of the expected design model. This analysis is done using the dominant industry standard, OpenTelemetry, and so should apply to a wide range of distributed systems.

Shawn M. Reynolds, Hassan Reza

Blockchain interoperability enables independent blockchain systems to communicate and exchange assets across heterogeneous networks. However, the lack of comprehensive security mechanisms remains a critical weakness -- one that attackers have already exploited to cause hundreds of millions of dollars in asset losses. This paper presents a systematic identification and classification of security threats facing interoperable blockchain systems, along with corresponding countermeasures for each. We organize threats into five categories: (1) core blockchain attacks, (2) network attacks, (3) interoperability-specific attacks, (4) social engineering, and (5) code vulnerabilities, with particular attention to smart contract weaknesses. For each identified threat, we analyze its attack surface and propose effective defensive strategies. The resulting taxonomy provides a structured foundation for designing and evaluating secure blockchain interoperability solutions.

Orimoloye Folorunsho, Hassan Reza

Software testing is critical for verifying that systems meet specified requirements, yet remains among the most time-consuming and expensive activities in development. Requirements-based test generation allows test cases to be derived early from requirements artifacts, but generating them directly from natural language is challenging due to inherent ambiguity and imprecision. Recent advances in AI, natural language processing (NLP), and large language models (LLMs) have made automating this pipeline increasingly feasible, while introducing new risks including hallucination, reduced traceability, and inconsistent evaluation. This survey addresses four research questions: what AI and NLP techniques have been proposed for generating test cases from natural language requirements; what tools and frameworks support these approaches; how generated test cases are evaluated; and what research gaps remain. Following Kitchenham and Charters' systematic review guidelines, we searched major scholarly databases spanning 2000-2025 and, after applying strict inclusion criteria, identified 21 primary studies. The literature is organized into three evolutionary eras, revealing that no existing approach simultaneously satisfies six key quality dimensions: automation, ambiguity handling, domain applicability, traceability, evaluation thoroughness, and hallucination control. The survey makes three main contributions: a three-era evolutionary synthesis of AI-based test generation; a six-criteria gap analysis showing no current approach fully addresses all quality dimensions; and four actionable research guidelines targeting hallucination, traceability, complexity sensitivity, and compliance.