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Evidence-driven Testing and Debugging of Software Systems
Soremekun, Ezekiel ; Herrmann, Andrea

testing debugging evidence-driven dynamic slicing statistical debugging
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2

Evidence-driven testing and debugging of software systems
Soremekun, Ezekiel Olamide ; Zeller, Andreas

ddc:004 ddc:500 ddc:600
Dissertation
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3

Search-Based Software Engineering: Trends, Techniques and Applications.
HARMAN, MARK ; MANSOURI, S. AFSHIN ; YUANYUAN ZHANG
ACM Computing Surveys. Nov2012, Vol. 45 Issue 1, p11:1-11:61. 61p. 4 Diagrams, 14 Charts, 2 Graphs.

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4

Explainability in Deep Reinforcement Learning: A Review into Current Methods and Applications.
Hickling, Thomas ; Zenati, Abdelhafid ; Aouf, Nabil ; et al.
ACM Computing Surveys. May2024, Vol. 56 Issue 5, p1-35. 35p.

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5

Understanding cost drivers of software evolution: a quantitative and qualitative investigation of change effort in two evolving software systems.
Benestad, Hans ; Anda, Bente ; Arisholm, Erik
Empirical Software Engineering; Apr2010, Vol. 15 Issue 2, p166-203, 38p, 2 Diagrams, 7 Charts, 6 Graphs

COMPUTER software COMPUTER systems QUANTITATIVE research QUALITATIVE research COST ORGANIZATION
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6

STAMINA: a competition to encourage the development and assessment of software model inference techniques.
Walkinshaw, Neil ; Lambeau, Bernard ; Damas, Christophe ; et al.
Empirical Software Engineering; Aug2013, Vol. 18 Issue 4, p791-824, 34p

SOFTWARE engineering COMPUTER software develo... MODEL-driven software ar... SOFTWARE maintenance COMPUTER programming SOFTWARE reengineering
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7

THE SURPRISING POWER OF ONLINE EXPERIMENTS: GETTING THE MOST OUT OF A/B AND OTHER CONTROLLED TESTS.
Kohavi, Ron ; Thomke, Stefan
Harvard Business Review. Sep/Oct2017, Vol. 95 Issue 5, p74-82. 9p. 1 Color Photograph, 1 Diagram, 1 Graph.

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8

Automatic Test Set Generation for Event-Driven Systems in the Absence of Specifications Combining Testing with Model Inference.
Novella, Luigi ; Tufo, Manuela ; Fiengo, Giovanni
Information Technology & Control; 2019, Vol. 48 Issue 2, p316-334, 19p

GRAPHICAL user interface... SOFTWARE reliability TECHNICAL specifications MACHINE learning TESTING
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9

A Modular Solution Concept for Self-Configurable Electronic Lab Notebooks: Systematic Theoretical Demonstration and Validation Across Diverse Digital Platforms.
Feldhoff, Kim ; Zinner, Martin ; Wiemer, Hajo ; et al.
Applied Sciences (2076-3417); Jan2026, Vol. 16 Issue 1, p462, 49p

COST effectiveness CUSTOMIZATION SCALABILITY DIGITAL technology ADAPTIVE computing syste... REGULATORY compliance
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10

Leveraging multi-agent framework for root cause analysis.
Fu, Feng ; Ding, Hong ; Qin, Yong ; et al.
Complex & Intelligent Systems; Jan2026, Vol. 12 Issue 1, p1-13, 13p

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11

Multi-Agent RAG Framework for Entity Resolution: Advancing Beyond Single-LLM Approaches with Specialized Agent Coordination.
Althaf, Aatif Muhammad ; Mohammed, Muzakkiruddin Ahmed ; Milanova, Mariofanna ; et al.
Computers (2073-431X); Dec2025, Vol. 14 Issue 12, p525, 30p

MULTIAGENT systems LANGUAGE models CLUSTERING algorithms DATA integration
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12

Analyzing the enhancement of CNN-YOLO and transformer based architectures for real-time animal detection in complex ecological environments.
Raza, Ali ; Hanif, Fareeha ; Mohammed, Heba Abdelgader
Scientific Reports; 11/7/2025, Vol. 15 Issue 1, p1-33, 33p

TRANSFORMER models REAL-time computing ECOSYSTEMS COMPUTER vision MACHINE learning EDGE computing …
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13

Big Data Analytics in IoT, social media, NLP, and information security: trends, challenges, and applications.
Taha, Kamal
Journal of Big Data; 6/17/2025, Vol. 12 Issue 1, p1-91, 91p

GRAPH neural networks INFORMATION technology s... INFORMATION storage & re... ARTIFICIAL intelligence DATA analytics NATURAL language process... …
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14

How do ML practitioners perceive explainability? an interview study of practices and challenges.
Habiba, Umm-e- ; Habib, Mohammad Kasra ; Bogner, Justus ; et al.
Empirical Software Engineering; Feb2025, Vol. 30 Issue 1, p1-25, 25p

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15

Generative AI in Mechanical Engineering Education: Enablers, Challenges, and Implementation Pathways.
Alghazo, Mohannad ; Ahmed, Vian ; Bahroun, Zied ; et al.
Sustainability (2071-1050); Dec2025, Vol. 17 Issue 23, p10817, 34p

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16

Quality assurance methodologies for automated driving.
Wotawa, Franz ; Peischl, Bernhard ; Klück, Florian ; et al.
e & i Elektrotechnik und Informationstechnik; Aug2018, Vol. 135 Issue 4/5, p322-327, 6p

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17

Multioracle Coevolutionary Learning of Requirements Specifications from Examples in On-The-Fly Markets.
Wever, Marcel ; van Rooijen, Lorijn ; Hamann, Heiko
Evolutionary Computation; Summer2020, Vol. 28 Issue 2, p165-193, 29p

REQUIREMENTS engineering MACHINE learning SOFTWARE engineering SYSTEMS software PRODUCTION engineering
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18

From conception to fruition: Co-designing a digital exhibit incorporating embodied cognition to encourage young children's computational thinking in a Science Discovery Centre.
Murcia, Karen ; Lowe, Geoffrey ; Mavilidi, Myrto ; et al.
Australian Educational Researcher (Springer Science & Business Media B.V.); Jul2025, Vol. 52 Issue 3, p1751-1772, 22p

COGNITIVE psychology IDENTIFICATION (Psycholo... DIGITAL literacy LONG-term memory COGNITIVE load MENTAL representation
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19

Cocreating an Automated mHealth Apps Systematic Review Process With Generative AI: Design Science Research Approach.
Giunti, Guido ; Doherty, Colin P
JMIR Medical Education; 2024, Vol. 10, p1-1656, 1656p

MOBILE health GENERATIVE artificial in...
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20

Explainable reinforcement learning (XRL): a systematic literature review and taxonomy.
Bekkemoen, Yanzhe
Machine Learning; Jan2024, Vol. 113 Issue 1, p355-441, 87p

REINFORCEMENT learning EVIDENCE gaps TAXONOMY DEEP learning RESEARCH personnel
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