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Mr Tamas Galli

Job: PhD student

Faculty: Computing, Engineering and Media

School/department: School of Computer Science and Informatics

Address: De Montfort University, The Gateway, Leicester, LE1 9BH

T: N/A

E: tamas.galli@my365.dmu.ac.uk

 

Personal profile

Tamas Galli is a PhD researcher at the Institute of Artificial Intelligence of the De Montfort University, Leicester, UK. His research includes modelling the subjective manifestation of uncertainty, missing and contradicting information by means of computational intelligence methods in the field of software product quality assessments. Tamas holds an MPhil degree in Computer Science from the De Montfort University, Leicester, UK, two BEng degrees in Electrical and Electronic Engineering from the Kando Kalman College of Engineering, Budapest, Hungary. He received the following international industrial and professional certificates: Certified Scrum Master (Scrum Alliance), Chartered IT Professional Member of the British Computer Society, Sun Certified Enterprise Architect, Oracle Certified Expert: Java EE Web Component Developer, Oracle Certified Professional: Java Programmer, Sun Certified Web Component Developer, Sun Certified Business Component Developer, Sun Certified Java Programmer, Oracle PL/SQL Certified Developer Associate. Tamas pursues his research part-time while working as a software engineer full-time.

Research group affiliations

Publications and outputs

Galli, Tamas, Francisco Chiclana, and Francois Siewe. 2021. "On the Use of Quality Models to Address Distinct Quality Views" Applied System Innovation 4, no. 3: 41. https://doi.org/10.3390/asi4030041 

Galli, Tamas; Chiclana, Francisco; Siewe, Francois, Quality Properties of Execution Tracing, An Empirical Study, Appl. Syst. Innov. 4, no. 1: 20, 2021, MDPI, https://doi.org/10.3390/asi4010020 

Tamas Galli, Francisco Chiclana, Francois Siewe, Performance of Execution Tracing with Aspect-Oriented and Conventional Approaches. In: Gupta, C., Gupta, V. (eds.) Research and Evidence in Software Engineering: From Empirical Studies to Open Source Artifacts, 2020, Auerbach Publications, Boca Raton, FL: CRC Press, ISBN 9780367358525

Tamas Galli, Francisco Chiclana, Francois Siewe, Software Product Quality Models, Developments, Trends, and Evaluation, SN Computer Science, 2020, Springer, doi: 10.1007/s42979-020-00140-z

T. Nagy, T. Galli, G. Nagy, E. Szántó, J. Simon (2018) Bacteria classification problem in the laboratory diagnosis of black pigmented anaerobic bacterial infections, 59th National Congress of the Hungarian Society of Laboratory Medicine, Pécs, Hungary, 2018, Abstract In: Clinical Chemistry and Laboratory Medicine 2018; 56(9): eA121–eA169, SE3.9

Tamas Galli, Francisco Chiclana, Jenny Carter, Helge Janicke, “Towards Introducing Execution Tracing to Software Product Quality Frameworks,” Acta Polytechnica Hungarica, vol. 11, no. 3, pp. 5-24, 2014. doi: 10.12700/APH.11.03.2014.03.1

Tamas Galli, Francisco Chiclana, Jenny Carter, Helge Janicke “Modelling Execution Tracing Quality by Means of Type-1 Fuzzy Logic,” Acta Polytechnica Hungarica, vol. 10, no. 8, pp. 49-67, 2013. doi: 10.12700/APH.10.08.2013.8.3

Research interests/expertise

Software Product Quality Modelling, Fuzzy Logic, Adaptive Neuro-Fuzzy Inference Systems, Data Analytics, Data Science

Qualifications

  • MPhil, BEng, BEng, Certified Scrum Master (Scrum Alliance)
  • Chartered IT Professional Member of the British Computer Society 
  • Sun Certified Enterprise Architect
  • Oracle Certified Expert: Java EE Web Component Developer
  • Oracle Certified Professional: Java Programmer
  • Sun Certified Web Component Developer
  • Sun Certified Business Component Developer 
  • Sun Certified Java Programmer
  • Oracle PL/SQL Certified Developer Associate

PhD project

Title

Application of Data Science Techniques for Modelling Execution Tracing Quality

Abstract

The research being conducted elicits the software product quality frameworks which attempt to describe each property of software product quality and classifies them. Moreover, a model is being developed to describe the quality of execution tracing with regard to the subjective manifestation of uncertainty, inherently present in the quality measurement process, for approximating the reality better. After developing the quality model for execution tracing, it will be linked to a software product quality framework to ensure the ability of assessing execution tracing in the scope of the overall quality assessment process.

The research is a mixed-mode research comprising of both qualitative and quantitative methods. Qualitative research is conducted to identify the quality properties of execution tracing while quantitative research is performed to describe the effects of the quality properties in the output variable: software product quality. For the latter an adaptive neuro-fuzzy inference system will be used.

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