International Journal of Software Computing and Testing

International Journal of Software Computing and Testing

International Journal of Software Computing and Testing [2456-2351(e)] is a peer-reviewed hybrid open-access journal launched in 2015. It welcomes research papers and editorial reviews concerning the development of software computing and testing. Fuzzy computing, hybrid methods, immunological computing, and morphic computing are a few topics that are included in the scope and focus of the journal.

• Fuzzy systems and approximate reasoning: fuzzy set theory and membership function design, fuzzy inference systems and rule base construction, type-2 and interval-valued fuzzy systems, fuzzy control and decision support, linguistic variable modelling, and reasoning under vagueness and imprecision.
• Neural and neuro-fuzzy computing: feedforward and recurrent network architectures, learning algorithms and convergence behaviour, adaptive neuro-fuzzy inference systems, function approximation and universal approximation properties, associative memory models, and hybrid neural–symbolic architectures.
• Evolutionary computation: genetic algorithms and genetic programming, evolution strategies and differential evolution, multi-objective and many-objective optimisation, fitness landscape analysis, constraint handling, coevolution, and parameter control and self-adaptation.
• Swarm intelligence and population-based methods: particle swarm optimisation and its variants, ant colony and bee colony algorithms, multi-swarm and cooperative strategies, particle diversity and premature convergence, and hybrid swarm approaches for continuous and discrete problems.
• Hybrid and memetic computing: combinations of fuzzy, neural, and evolutionary paradigms, memetic algorithms with local search, hyper-heuristics and algorithm selection, ensemble metaheuristics, and empirical comparison of hybrid designs.
• Nature-inspired and emerging computing paradigms: artificial immune systems and immunological computing, simulated annealing and physics-inspired methods, quantum-inspired evolutionary algorithms, granular and rough set computing, and rigorous evaluation of novel metaheuristics against established baselines.
• Search-based software engineering: formulation of software engineering problems as search problems, metaheuristic approaches to requirements selection and release planning, search-based refactoring and design optimisation, effort and cost estimation using computational intelligence, and empirical evaluation of search-based techniques.
• Automated test generation: search-based and evolutionary test data generation, coverage-guided fuzzing, symbolic and concolic test generation, model-based test derivation, mutation-driven test generation, and generation of tests for concurrent and non-deterministic systems.
• Software testing methods and levels: unit, integration, and system testing strategies, regression test selection and prioritisation, combinatorial and pairwise interaction testing, user-session and usage-based testing, testing of web, mobile, and distributed applications, and testing of machine learning and data-driven systems.
• Test optimisation and adequacy: test suite minimisation and reduction, coverage criteria and their effectiveness, mutation analysis and mutation score, test oracle design and the oracle problem, flaky test detection, and cost–benefit analysis of testing strategies.
• Defect prediction and software quality modelling: fault-proneness prediction using computational intelligence, software metrics and feature selection for quality models, imbalanced data handling in defect datasets, cross-project and cross-version prediction, dataset quality and provenance, and reliability growth modelling.
• Verification, validation, and fault diagnosis: model checking and formal verification, runtime verification and monitoring, automated fault localisation and program repair, anomaly detection in software behaviour, and combined analytical and intelligent approaches to assurance.
• Applications of soft computing: optimisation in engineering design and scheduling, pattern recognition and classification tasks, forecasting and time series modelling, resource allocation in computing systems, and decision support in complex and uncertain domains.