arXiv subjects

Bedir Tekinerdogan

Publications and source records attributed to Bedir Tekinerdogan.

12 recordsLinked to original sources

arXiv2026

AI4PLE: A Methodology for Integrating AI into Product Line Engineering

Reuse-based development has become increasingly important in the creation of complex systems, offering significant opportunities to reduce costs, improve quality, and accelerate time-to-market. Product Line Engineering (PLE) provides a systematic approach to realizing this potential by enabling the efficient creation, management, and customization of product families by reusing shared assets and capabilities. PLE involves addressing numerous complex decisions, including feature selection, variability management, and configuration optimization, which are critical to the success of a product line. Despite its promise, the systematic integration of Artificial Intelligence (AI) into PLE processes has not yet been comprehensively explored. In this paper, we propose a methodological framework to support the systematic integration of AI into PLE and evaluate its effectiveness through a multi-case study conducted in an industrial context.

cs.AI↗

arXiv2025

Evidence-Driven Decision Support for AI Model Selection in Research Software Engineering

The rapid proliferation of artificial intelligence (AI) models and methods presents growing challenges for research software engineers and researchers who must select, integrate, and maintain appropriate models within complex research workflows. Model selection is often performed in an ad hoc manner, relying on fragmented metadata and individual expertise, which can undermine reproducibility, transparency, and overall research software quality. This work proposes a structured and evidence-driven approach to support AI model selection that aligns with both technical and contextual requirements. We conceptualize AI model selection as a Multi-Criteria Decision-Making (MCDM) problem and introduce an evidence-based decision-support framework that integrates automated data collection pipelines, a structured knowledge graph, and MCDM principles. Following the Design Science Research methodology, the proposed framework (ModelSelect) is empirically validated through 50 real-world case studies and comparative experiments against leading generative AI systems. The evaluation results show that ModelSelect produces reliable, interpretable, and reproducible recommendations that closely align with expert reasoning. Across the case studies, the framework achieved high coverage and strong rationale alignment in both model and library recommendation tasks, performing comparably to generative AI assistants while offering superior traceability and consistency. By framing AI model selection as an MCDM problem, this work establishes a rigorous foundation for transparent and reproducible decision support in research software engineering. The proposed framework provides a scalable and explainable pathway for integrating empirical evidence into AI model recommendation processes, ultimately improving the quality and robustness of research software decision-making.

cs.SE↗

arXiv2025

Empirical Evaluation of AI-Assisted Software Package Selection: A Knowledge Graph Approach

Selecting third-party software packages in open-source ecosystems like Python is challenging due to the large number of alternatives and limited transparent evidence for comparison. Generative AI tools are increasingly used in development workflows, but their suggestions often overlook dependency evaluation, emphasize popularity over suitability, and lack reproducibility. This creates risks for projects that require transparency, long-term reliability, maintainability, and informed architectural decisions. This study formulates software package selection as a Multi-Criteria Decision-Making (MCDM) problem and proposes a data-driven framework for technology evaluation. Automated data pipelines continuously collect and integrate software metadata, usage trends, vulnerability information, and developer sentiment from GitHub, PyPI, and Stack Overflow. These data are structured into a decision model representing relationships among packages, domain features, and quality attributes. The framework is implemented in PySelect, a decision support system that uses large language models to interpret user intent and query the model to identify contextually appropriate packages. The approach is evaluated using 798,669 Python scripts from 16,887 GitHub repositories and a user study based on the Technology Acceptance Model. Results show high data extraction precision, improved recommendation quality over generative AI baselines, and positive user evaluations of usefulness and ease of use. This work introduces a scalable, interpretable, and reproducible framework that supports evidence-based software selection using MCDM principles, empirical data, and AI-assisted intent modeling.

cs.SE↗

arXiv2024

A Tool for Automated Reasoning About Traces Based on Configurable Formal Semantics

We present Tarski, a tool for specifying configurable trace semantics to facilitate automated reasoning about traces. Software development projects require that various types of traces be modeled between and within development artifacts. For any given artifact (e.g., requirements, architecture models and source code), Tarski allows the user to specify new trace types and their configurable semantics, while, using the semantics, it automatically infers new traces based on existing traces provided by the user, and checks the consistency of traces. It has been evaluated on three industrial case studies in the automotive domain (https://modelwriter.github.io/Tarski/).

cs.SE↗

arXiv2024

AlloyInEcore: Embedding of First-Order Relational Logic into Meta-Object Facility for Automated Model Reasoning

We present AlloyInEcore, a tool for specifying metamodels with their static semantics to facilitate automated, formal reasoning on models. Software development projects require that software systems be specified in various models (e.g., requirements models, architecture models, test models, and source code). It is crucial to reason about those models to ensure the correct and complete system specifications. AlloyInEcore allows the user to specify metamodels with their static semantics, while, using the semantics, it automatically detects inconsistent models, and completes partial models. It has been evaluated on three industrial case studies in the automotive domain (https://modelwriter.github.io/AlloyInEcore/).

cs.SE↗

arXiv2024

ModelWriter: Text & Model-Synchronized Document Engineering Platform

The ModelWriter platform provides a generic framework for automated traceability analysis. In this paper, we demonstrate how this framework can be used to trace the consistency and completeness of technical documents that consist of a set of System Installation Design Principles used by Airbus to ensure the correctness of aircraft system installation. We show in particular, how the platform allows the integration of two types of reasoning: reasoning about the meaning of text using semantic parsing and description logic theorem proving; and reasoning about document structure using first-order relational logic and finite model finding for traceability analysis.

cs.SE↗

arXiv2022

On the Use of Deep Learning in Software Defect Prediction

Context: Automated software defect prediction (SDP) methods are increasingly applied, often with the use of machine learning (ML) techniques. Yet, the existing ML-based approaches require manually extracted features, which are cumbersome, time consuming and hardly capture the semantic information reported in bug reporting tools. Deep learning (DL) techniques provide practitioners with the opportunities to automatically extract and learn from more complex and high-dimensional data. Objective: The purpose of this study is to systematically identify, analyze, summarize, and synthesize the current state of the utilization of DL algorithms for SDP in the literature. Method: We systematically selected a pool of 102 peer-reviewed studies and then conducted a quantitative and qualitative analysis using the data extracted from these studies. Results: Main highlights include: (1) most studies applied supervised DL; (2) two third of the studies used metrics as an input to DL algorithms; (3) Convolutional Neural Network is the most frequently used DL algorithm. Conclusion: Based on our findings, we propose to (1) develop more comprehensive DL approaches that automatically capture the needed features; (2) use diverse software artifacts other than source code; (3) adopt data augmentation techniques to tackle the class imbalance problem; (4) publish replication packages.

cs.SE↗

arXiv2021

Feature-Driven Survey of Physical Protection Systems

Many systems nowadays require protection against security or safety threats. A physical protection system (PPS) integrates people, procedures, and equipment to protect assets or facilities. PPSs have targeted various systems, including airports, rail transport, highways, hospitals, bridges, the electricity grid, dams, power plants, seaports, oil refineries, and water systems. Hence, PPSs are characterized by a broad set of features, from which part is common, while other features are variant and depend on the particular system to be developed. The notion of PPS has been broadly addressed in the literature, and even domain-specific PPS development methods have been proposed. However, the common and variant features are fragmented across many studies. This situation seriously impedes the identification of the required features and likewise the guidance of the systems engineering process of PPSs. To enhance the understanding and support the guidance of the development of PPS, in this paper, we provide a feature-driven survey of PPSs. The approach applies a systematic domain analysis process based on the state-of-the-art of PPSs. It presents a family feature model that defines the common and variant features and herewith the configuration space of PPSs

cs.SE↗

arXiv2020

Adoption of ICT innovations in the agriculture sector in Africa: A Systematic Literature Review

According to the latest World Economic Forum report, about 70% of the African population depends on agriculture for their livelihood. This makes agriculture a critical sector within the African continent. Nonetheless, agricultural productivity is low and food insecurity is still a challenge. This has in recent years led to several initiatives in using ICT (Information Communication Technology) to improve agriculture productivity. This study aims to explore ICT innovations in the agriculture sector of Africa. To achieve this, we conducted a SLR (Systematic Literature Review) of the literature published since 2010. Our search yielded 779 papers, of which 23 papers were selected for a detailed analysis following a detailed exclusion and quality assessment criteria. The analysis of the selected papers shows that the main ICT technologies adopted are text and voice-based services targeting mobile phones. The analysis also shows that radios are still widely used in disseminating agriculture information to rural farmers, while computers are mainly used by researchers. Though the mobile-based services aimed at improving access to accurate and timely agriculture information, the literature reviews indicate that the adoption of the services is constrained by poor technological infrastructure, inappropriate ICT policies and low capacity levels of users, especially farmers, to using the technologies. The findings further indicate that literature on an appropriate theoretical framework for guiding ICT innovations is lacking.

cs.CY↗

arXiv2020

Model-Based User Interface Design for Generating E-Forms in the Context of an E-Government Project

We report on our experiences in an e-government project for supporting the automatic generation of E-forms for services provided by local governments. The approach requires the integration of both the model-based user interface design (MBUID) and software product line engineering approaches. During the domain engineering activity the commonality and variability of product services is modeled using feature diagrams and the corresponding UI models are defined. To support the automation of e-forms the implemented feature models are on their turn used to generate E-forms automatically to enhance productivity, increase quality and reduce cost of development. We have developed three different approaches for e-form generation in increasing complexity: (1) offline model transformation without interaction (2) model transformation with initial interaction (3) model-transformation with run-time interaction. We discuss the lessons learned and propose a systematic approach for defining model transformations that is based on an interactive paradigm.

cs.SE↗

arXiv2020

Analyzing the Impact of Automated User Assistance Systems: A Systematic Review

Context: User assistance is generally defined as the guided assistance to a user of a software system in order to help accomplish tasks and enhance user experience. Automated user assistance systems are equipped with online help system that provides information to the user in an electronic format and which can be opened directly in the application. Various different automated user assistance approaches have been proposed in the literature. However, there has been no attempt to systematically review and report the impact of automated user assistance systems. Objective: The overall objective of this systematic review is to identify the state of art in automated user assistance systems, and describe the reported evidence for automated user assistance. Method: A systematic literature review is conducted by a multiphase study selection process using the published literature since 2002. Results: We reviewed 575 papers that are discovered using a well-planned review protocol, and 31 of them were assessed as primary studies related to our research questions. Conclusions: Our study shows that user assistance systems can provide important benefits for the user but still more research is required in this domain.

cs.HC↗

arXiv2020

Experience in engineering of scientific software: The case of an optimization software for oil pipelines

Development of scientific and engineering software is usually different and could be more challenging than the development of conventional enterprise software. The authors were involved in a technology-transfer project between academia and industry which focused on engineering, development and testing of a software for optimization of pumping energy costs for oil pipelines. Experts with different skillsets (mechanical, power and software engineers) were involved. Given the complex nature of the software (a sophisticated underlying optimization model) and having experts from different fields, there were challenges in various software engineering aspects of the software system (e.g., requirements and testing). We report our observations and experience in addressing those challenges during our technology-transfer project, and aim to add to the existing body of experience and evidence in engineering of scientific and engineering software. We believe that our observations, experience and lessons learnt could be useful for other researchers and practitioners in engineering of other scientific and engineering software systems.

cs.SE↗