Abstract Knowledge management systems (KMS) are computer‐based systems highly valued in business organizations because they support knowledge management (KM) processes. Most KMS have been developed using non‐intelligent computer technology—that is, DMS, CMS, DBMS, and CIS—, and thus, they cannot provide advanced capabilities. Consequently, enhanced KMS using intelligent technologies of ontologies with inference engines—called ontology‐based knowledge management systems (OKMS)—have been proposed in the last three decades. Nowadays, however, the implementation of OKMS in real‐world settings is still scarce. Lack of comprehensive and systematic development methodologies including Project Management and Technical Systems Engineering processes—as the Systems and Software Systems Engineering standards propose—have been suggested as a factor that inhibits OKMS implementations. In this study, we review the OKMS literature (1990–2021 period)—from six seminal studies located using a research search engine—to assess OKMS development methodologies that can be considered comprehensive and systematic. Five methodologies were identified and assessed using an evaluation subset from the ISO/IEC/IEEE 15288:2015 Systems and Software Engineering standard. Two of them—CommonKADS and NeON—were found with a high comprehensive and systematic level and both are suggested for organizations interested in OKMS implementations, but none of them qualified as agile, which is a current development approach for systems and software systems. Hence, further empirical research toward the realization of comprehensive and systematic OKMSs development methodologies, including agile versions, is suggested for fostering the implementation of OKMS in real‐world settings.

The design of the system architecture has been a critical and relevant activity in the systems engineering discipline from several decades for the design of physical man-made systems [1].However, a reduced set of design methods and complementary decision-making techniques have been consistently used with successful results [2][3][4].In contrast, in the software engineering discipline, there is a body of academic research on the foundations, methods and complementary techniques with a scarce accumulative knowledge.Every design stream on software architecture elaborates its own vocabulary and methods and usually ignores other design streams [5].Additionally, there are more proprietary than open access design methods, and a reduced set of well-accepted methods, techniques and practices are still missing [6].Thus, software architecture design methods are inconsistently or little used in the practice during the application of software development methodologies except by the large system and software development companies [7,8].As a pioneer [9] in the software architecture domain indicates (idem, p. 29) However, despite this progress, as engineering disciplines go, the field of software architecture remains relatively immature.While the foundations of an engineering basis for software architecture are now clear, there remain numerous challenges and unknowns.In particular, software systems are widely demanded at present in two co-existing paradigms: Software-oriented Computing (SOC) [10,11] and the Cloud Computing paradigms [12,13].SOC paradigm aims the development of medium-sized and large-distributed inter-organizational systems by assembling it (ideally) as a system of service providers and consumers, known as a Service-oriented Computing Application (SOCA) [14].Thus, the design of SOCAs inherently includes the challenges of high-quality software architecture design, given its natural composition by services provided by some entities and consumed by others (independently of the self-services in the same SOCA).In turn, the Cloud Computing paradigm aims to provide a cost-effective, scalable and dynamic provision of IT resources (processing, storage, networking) for elastic demands.This convergence of SOC and this emergent paradigm introduces new software

The design, build and evaluation of an ontology-based knowledge management system prototype tool — entitled CMMIoKMS — for supporting the consultation and understanding of CMMI for Development (CMMI-DEV) is reported. Ontology-based tools have been proposed for improving such complex cognitive tasks realized by Software Engineering practitioners involved in CMMI implementations. The design of the CMMI-oKMS prototype tool is based on four proprietary ontologies (of things, of systems, of business, and of CMMI models) and built with Protégé tool. The result from a pilot empirical test, is reported as being satisfactory — compared with the extant reported systems — on the metric of perceived value (with statistical significance) and over usefulness, compatibility, and test score constructs (with practical significance), while that no gain was found in ease of use. Hence, given the limited number of similar KMS tools reported in the current literature, this research contributes to our body of knowledge in Software Engineering by providing useful design, building and evaluation insights for improving these knowledge support systems.

Implementing decision-making support systems (DMSS) is considered an organizationally complex and risky task that is influenced by dynamic technical and social-political issues. Consequently, DMSS implementation failures, with associated economic loses, are still reported. While several statistics-based (static) quantitative models of successful factors and qualitative (descriptive) models to implement DMSS are available, few quantitative dynamic models have been posed. In this paper, we illustrate how a dynamic simulation model of the DMSS implementation process can be designed. We use a system dynamics approach via an extended methodology, which is called critical realism-based methodology for studying soft systems dynamics. Validation is realized through 1) the theoretical validity of the model, 2) the model's capability in reproducing historical DMSS implementation paths, and 3) the model's capability in predicting new DMSS implementation paths from new cases. Simulation results suggest the adequacy of using these modeling methods to complement the knowledge on the DMSS implementation processes.