Christos Tjortjis

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This work proposes a methodology for source code quality and static behaviour evaluation of a software system, based on the standard ISO/IEC-9126. It uses elements automatically derived from source code enhanced with expert knowledge in the form of quality characteristic rankings, allowing software engineers to assign weights to source code attributes. It(More)
The epidemiological question of concern here is " can young children at risk of obesity be identified from their early growth records? " Pilot work using logistic regression to predict overweight and obese children demonstrated relatively limited success. Hence we investigate the incorporation of non-linear interactions to help improve accuracy of(More)
Accelerating the learning curve of software maintainers working on systems with which they have little familiarity motivated this study. A working hypothesis was that automated methods are needed to provide a fast, rough grasp of a system, to enable practitioners not familiar with it, to commence maintenance with a level of confidence as if they had this
Clustering is a data mining technique that allows the grouping of data points on the basis of their similarity with respect to multiple dimensions of measurement. It has also been applied in the software engineering domain, in particular to support software quality assessment based on source code metrics. Unfortunately, since clusters emerge from metrics at(More)
Data mining is a technology recently used in support of software maintenance in various contexts. Our works focuses on achieving a high level understanding of Java systems without prior familiarity with these. Our thesis is that system structure and interrelationships, as well as similarities among program components can be derived by applying cluster(More)
Data mining and its capacity to deal with large volumes of data and to uncover hidden patterns has been proposed as a means to support industrial scale software maintenance and comprehension. This paper presents a methodology for knowledge acquisition from source code in order to comprehend an object-oriented system and evaluate its maintainability. We(More)
Clustering is particularly useful in problems where there is little prior information about the data under analysis. This is usually the case when attempting to evaluate a software system's maintainability, as many dimensions must be taken into account in order to reach a conclusion. On the other hand partitional clustering algorithms suffer from being(More)
In this paper we propose an effective and efficient new Fuzzy Healthy Association Rule Mining Algorithm (FHARM) that produces more interesting and quality rules by introducing new quality measures. In this approach, edible attributes are filtered from transactional input data by projections and are then converted to Required Daily Allowance (RDA) numeric(More)
In recent years interest has grown in “mining” large databases to extract novel and interesting information. Knowledge Discovery in Databases (KDD) has been recognised as an emerging research area. Association rules discovery is an important KDD technique for better data understanding. This paper proposes an enhancement with a memory efficient data(More)