Software development is a knowledge-intensive activity, and managing knowledge effectively is essential to ensuring productivity and quality. Software reuse (SR) is a strategic approach that improves efficiency and return on investment, but sustaining reuse programs remains challenging. Organisations often struggle to manage the knowledge embedded in software assets such as code, designs, and test cases. Knowledge management (KM) processes—identification, acquisition, sharing, storage, codification, creation, application, and evaluation—offer a promising lens for addressing these challenges. Yet, recent work has paid limited attention to their explicit role in SR. This exploratory work analyses the recent literature on SR from a KM perspective, seeking to understand how the main knowledge processes support SR. To this end, a rapid literature review was conducted to identify studies that explicitly discuss KM aspects of SR. The review applied inclusion criteria focused on studies that explicitly discuss KM for SR, and exclusion criteria that removed unavailable studies, review-only studies, and studies that did not answer the research questions. Each selected study was then mapped, categorised, and analysed using a protocol aligned with the research questions, considering the KM processes addressed, the types of assets reused at each reuse level (specification reuse, design reuse, code reuse, application system reuse, and test reuse), and the research method employed. By structuring the evidence around these dimensions, the review helps characterise how knowledge processes are discussed in relation to different forms of reuse, different reusable artefacts, and both operational and strategic reuse concerns. Rather than consolidating a mature theory, the results indicate tentative paths for connecting KM and SR research. In particular, the analysis indicates a predominance of storage and codification, followed by application and sharing. At the same time, the findings highlight the need to deepen research on knowledge evaluation, test reuse, and the organisational conditions that help sustain software reuse initiatives over time.
Emilly Lopes, C. E. Barbosa, M. Argôlo et al.· European Conference on Knowl...· 0 citations
The externalisation of tacit knowledge, defined as practical, experiential knowledge that is difficult to formalise, represents one of the greatest challenges faced by institutions that rely on the expertise of their members. Due to its subjective and contextual nature, this type of knowledge resists capture through traditional methods such as interviews and observations, which are costly, difficult to standardise, and limited in reusability. This work proposes an architecture called K-GRASP (Knowledge Graph-based Retrieval-Augmented Structured Prompting), which combines the representational power of knowledge graphs (KG) with the probabilistic reasoning capabilities of Large Language Models (LLMs) to address this challenge. The proposed solution is divided into two phases: (i) a capture phase, in which an interviewer LLM conducts structured sessions with experts and converts their responses into RDF triples stored in a semantic graph, and (ii) a retrieval phase, in which a consultative LLM uses Retrieval-Augmented Generation (RAG) to translate natural language questions into SPARQL queries (SPARQL Protocol and RDF Query Language), retrieve relevant subgraphs, and generate contextualized responses. The architecture enables the systematic, reusable, and accessible codification of tacit knowledge, allowing for its large-scale preservation and dissemination. By integrating LLMs with formal representation structures, K-GRASP offers a robust, scalable, and interpretable solution to a historically complex problem in knowledge management. In the capture phase, prompts can be shaped to elicit concrete cases, boundary conditions, and decision rationales. At the same time, responses are mapped to an agreed-upon vocabulary to reduce drift across sessions. The graph can also retain provenance and scope cues (e.g., source expert, date, and stated assumptions), which may support later review and incremental refinement. In the retrieval phase, returning both the synthesised answer and the underlying triples can make the consultation more transparent and highlight gaps or ambiguities, as well as operational concerns such as access control, privacy, and versioning.
Rafael Luna, Gabriel S. Luna, C. E. Barbosa et al.· European Conference on Knowl...· 0 citations