The global software industry is predominantly composed of micro, small, and medium-sized enterprises (MSMEs), highlighting the need for software quality management to ensure the proper functioning and quality of the software. This research focuses on the evaluation of the implementation of the ISO/IEC 29110 standard work products, which is a standard tailored by the ISO/IEC specifically for MSMEs, which improves the software development process by implementing two processes in its basic profile: Project Management (PM) and Software Implementation (SI). Despite this standard being tailored specifically for this type of enterprise, implementing ISO/IEC 29110 faces several challenges, such as a lack of knowledge and difficulties in adequately implementing the work products regarding the compliance of standard criteria, among others. To address these challenges, we introduce VSEST 29110, a web tool designed to evaluate the ISO/IEC 29110 standard implementation work products by leveraging Artificial Intelligence (AI) technologies, specifically the ChatGPT model, provide detailed feedback on compliance with standard criteria, offer suggestions for improvement based on ChatGPT analysis and streamline the implementation process for MSMEs. To achieve this, our research incorporates a systematic literature review and validation through a case study by document analysis, demonstrating VSEST 29110’s effectiveness in enhancing compliance and providing comprehensive feedback compared to auditor recommendations, which impacts 69.33% on average.

El estándar ISO/IEC 29110 está específicamente orientado a muy pequeñas organizaciones ya que se adapta al contexto y necesidades de este tipo de organizaciones. Tras haber analizado las estructuras de los procesos: actividades, roles, tareas, y productos de trabajo considerados dentro del estándar ISO/IEC 29110 en su perfil básico se identificó falta de información acerca de los estados de los productos de trabajo que son necesarios para desarrollar las tareas, lo que dificulta su correcta implementación. Para ayudar a resolver esta problemática este artículo presenta propuestas de tautologías de lógica matemática. Como resultado se presentan propuestas de mejora en el establecimiento de estados a productos de trabajo que ayudarán en el control de la versión de los productos de trabajo y su documentación asociada.

In the context of proteomic-scale research, it is imperative to automatically analyze numerous species and subspecies to discern distinctive characteristics present in multiple species of the fungi kingdom that contain sequences of interest that could fulfill a specific biological function. To achieve this, complex sequences must be recognized within an organism’s entire set of proteomes. Our study presents FungiRegEx, a piece of software that facilitates the identification of regular expressions of proteomes of fungal organisms and uses real-time data retrieval of the different species from the JGI Mycocosm database without the need to download any file. Integrating a graphical user interface that makes it easy to use, the tool offers regular expression searches on 2402 fungal species from the JGI Mycocosm portal. The tool was validated with the AXSXG sequence and the RXRL effector, demonstrating the effectiveness of FungiRegEx in identifying user-defined patterns in the recovered sequences. This tool allows customization and filtering, and it can save results if required, combining speed, adaptability, and ease of use. It provides an experience without a console and programming, displaying the results in a GUI and making them easier to read. Its architecture guarantees optimized use of resources, time consumption, and implementation flexibility, allowing the customization of specific software parameters for resource management. The tool’s potential for future research and exploration is emphasized, providing a nuanced perspective on its practical use within the fungal genomics community. The tools are available at the addresses mentioned in the text.

Software projects often accumulate technical debt, which undermines code quality, maintainability, and long-term viability. This article analyses twelve categories of technical debt and examines the capabilities of existing mitigation tools. To achieve this, we conducted a systematic literature review. Based on the results, we categorise and describe each debt type as it occurs in practice, and we assess prominent tools (such as AnaConDebt, CAST, DebtFlag, Visminer TD, and TD-Tracker) with respect to the debt types they target, the programming languages they support, and their adherence to established software quality models or methodologies. Our analysis shows that most current solutions focus on code and architectural debt, whereas documentation and process debt remain largely unaddressed. Moreover, most tools do not conform to software quality models or standards, which none of them explicitly incorporate.

The increasing complexity and fragmentation of bioinformatics tools presents significant challenges for researchers, particularly those without extensive programming expertise. This research presents BioSuiteT v1.0, a unified web-based platform that integrates multiple bioinformatics tools into a single, user-friendly environment. BioSuiteT incorporates twelve core functionalities, including DNA and protein sequence analysis, transcription processes, sequence alignment, BLAST integration, PDB visualization and analysis, phylogenetic tree construction, MOTIFS analysis, and regular expression searching. The platform was developed using the Django framework and MongoDB, following ISO/IEC 29110 standards. Performance testing demonstrated the platform’s capability to handle concurrent users while maintaining stable performance with reduced memory usage. Validation testing involving eight researchers, five bioinformatics experts, and sixty practitioners showed a 95% reduction in technical support requirements compared to traditional installable tools and a 90% reduction in tool switching time. BioSuiteT effectively addresses key challenges identified in bioinformatics software usage, including installation barriers, programming expertise requirements, and tool fragmentation, while maintaining the necessary functionality for biological sequence analysis.