metrics – MECO

Unveiling Hybrid Cyclomatic Complexity: A Comprehensive Analysis and Evaluation as an Integral Feature in Automatic Defect Prediction Models (2025)

The complex software systems developed nowadays require assessing their quality and proneness to errors. Reducing code complexity is a never-ending problem, especially in today’s fast pace of software systems development. Therefore, the industry needs to find a method to determine the qualities of a software system, the degree of difficulty in developing new functionalities,…

Textural analysis and artificial intelligence as decision support tools in the diagnosis of multiple sclerosis – a systematic review (2025)

Introduction Magnetic resonance imaging (MRI) is conventionally used for the detection and diagnosis of multiple sclerosis (MS), often complemented by lumbar puncture—a highly invasive method—to validate the diagnosis. Additionally, MRI is periodically repeated to monitor disease progression and treatment efficacy. Recent research has focused on the application of artificial intelligence (AI) and radiomics in…

Challenges in Software Metrics Adoption: Insights from Cluj-Napoca’s Development Community (2025)

Established research directions yield concrete outcomes on the benefits of using software metrics in software development processes, such as notable correlations between software metric values and various quality attributes of software systems or defect prediction. A discrepancy exists between academic proposals and actual practices used in software development, influenced by factors like budget constraints,…

Image Reconstruction Using Cellular Automata and Neural Networks (2023)

Combining cellular automata (CA) with convolutional neural networks (CNN) has been overtime an important topic of scientific interest in the field of computer vision. This has been due to the high number of common aspects between the two models, such as extracting information and making decision based on pixel neighbourhoods. While there are many…

A pedagogical approach in interleaving software quality concerns at an artificial intelligence course (2022)

The software engineering industry is an everchanging domain requiring professionals to have a good knowledge base and adaptability skills.Artificial Intelligence (AI) has achieved substantial success in enhancing program analysis techniques and applications, including bug prediction. It is a promising direction by applying advanced Machine Learning techniques into suitable software engineering tasks. The main goal…

A Comparison of Meta-heuristic Based Optimization Methods Using Standard Benchmarks (2022)

Optimization problems are a type of problem in which multiple solutions satisfy the problem’s constraints, so not only must a good solution be found, but the objective is to find the best solution among all those considered valid. Optimization problems can be solved by using deterministic and stochastic algorithms. Those categories can be divided…

A Hybrid Complexity Metric in Automatic Software Defects Prediction (2022)

Nowadays, software systems evolve in vast and complex applications. In such a complex system, a minor change in one part may have unexpected degradation of the software system design, leading to an unending chain of bugs and defects. Therefore, to keep track of implications that could appear after a change has been applied, the…

On the use of evolutionary algorithms for test case prioritization in regression testing considering requirements dependencies (2021)

Nowadays, software systems encounter repeated modifications in order to satisfy any requirement regarding a business change. To assure that these changes do not affect systems' proper functioning, those parts affected by the changes need to be retested, minimizing the negative impact of performed modifications on another part of the software. In this research, we…

A Validation Framework for ARP Similarity Measure (2021)

In the last years, a wide range of new similarity measures has been designed and applied to different contexts. Currently, there’s a deep lack in the validation and evaluation steps for novel similarities. In general, new measures are validated mostly through numerical experiments using different data sets. But this is not enough, as in…