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When only women turn up to a panel on challenges for women in technology, how do we then reach out to industry, academia and government to encourage them to listen to the current challenges experienced by women in tech. Technology is rapidly changing and we are seeing women disadvantaged by less training opportunities, lack of role models, perceived penalties for taking time off to have children or discharge caring responsibilities as well as the risk that their jobs are subject to more automation. Multiple workshops at the Institute of Science and Technology highlighted significant challenges for women in tech, the data from our empirical study illustrates these challenges in detail. With the workplace still male dominated and the landscape changing rapidly, women have a significant role to play and we need to ensure that role is not only facilitated but the existing challenges are mitigated. This is a discussion paper with empirical data that illustrates challenges currently experienced by women in tech and how we can move forward to ensure not only equal opportunity but remove some of the challenges currently experienced. In this paper we have not considered the same impact on men who take career breaks for reasons of caring responsibilities. [ABSTRACT FROM AUTHOR]

Context: The knowledge domain of software engineering (SE) gradually expands due to fast emerging technologies and complex organisational processes. The software industry represents a special focus of interest from a Knowledge Management (KM) perspective, as it is characterized by high employee turnover, a high level of outsourced projects, and an accelerated pace of technology innovations. Knowledge management can substantially influence SE knowledge's life cycle, contributing to better structuring of organisational processes of knowledge creation, knowledge transfer, and knowledge sharing. Reflecting on the trends for SE automation with artificial intelligence tools, KM approaches can enable companies and professionals to reconsider the future of SE work. Purpose: The present research aims to identify and investigate how implementing knowledge management principles and processes within the software engineering domain can facilitate the future transformation of software-related jobs. Based on a conceptual model, built upon the literature review, the paper analyses the quantitative data, collected from 91 software experts. Evaluating the impact of both organizational and domain types of SE knowledge, the discussion evaluates the unique value-adding SE processes along with models of how to explore AI applications to automate the SE domain. Results: The outcomes of the analysis identify the models of structuring knowledge management processes in SE both within the technological domain and organisational layers. The discussion section establishes suitable KM methods and approaches for supporting SE job transformation. It identifies the main knowledge management integration practices, demonstrating how combining KM with AI-based innovations may change the SE knowledge flow, improving substantially SE activities like onboarding new team members, monitoring software documentation, version control, and error tracking. [ABSTRACT FROM AUTHOR]

An important aspect of knowledge management is the transfer of know-how between generations. This raises the question of how new employees, especially young professionals, can acquire this knowledge from older generations in an efficient and sustainable way. This can potentially necessitate the creation of incentives for both sides. For the older generation, the rewards must be of such a high quality that they are willing to pass on their expertise without seeing the younger generation as a threat. For the younger generation, ways must be created to accept the knowledge of the older generation as being of great value. In this paper, the authors discuss two methods that can facilitate this process. Gamification, to make the reward systems visible, and nudging, to trigger the benefits of these reward systems. Furthermore, by developing a working prototype, the authors describe how the actual rewards can be stored on Blockchain-based tokens in order to be represented in a permanent and provable way. This system can be supported by machine learning and the long-term preparation of adaptive learning systems, so that the knowledge of the older generation is ultimately made available to a broad group of people. The theoretical foundations are complemented by an online survey and a focus group with experts from the various fields in order to further corroborate the arguments made. [ABSTRACT FROM AUTHOR]