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Creativity has always been considered a major differentiator to separate the good from the great, and we believe the importance of creativity for software development will only increase as GenAI becomes embedded in developer tool-chains and working practices. This paper uses the McLuhan tetrad alongside scenarios of how GenAI may disrupt software development more broadly, to identify potential impacts GenAI may have on creativity within software development. The impacts are discussed along with a future research agenda comprising six connected themes that consider how individual capabilities, team capabilities, the product, unintended consequences, society, and human aspects can be affected.

Context: Scholars in the software engineering (SE) research community have investigated career advancement in the software industry. Research topics have included how individual and external factors can impact career mobility of software professionals, and how gender affects career advancement. However, the community has yet to look at career mobility from the lens of accessibility. Specifically, there is a pressing need to illuminate the factors that hinder the career mobility of blind and low vision software professionals (BLVSPs). Objective: This study aims to understand aspects of the workplace that impact career mobility for BLVSPs. Methods: We interviewed 26 BLVSPs with different roles, years of experience, and industry sectors. Thematic analysis was used to identify common factors related to career mobility. Results: We found four factors that impacted the career mobility of BLVSPs: (1) technical challenges, (2) colleagues' perceptions of BLVSPs, (3) BLVSPs' own perceptions on managerial progression, and (4) BLVSPs' investment in accessibility at the workplace. Conclusion: We suggest implications for tool designers, organizations, and researchers towards fostering more accessible workplaces to support the career mobility of BLVSPs., Comment: 12 pages, 1 table, conference paper, 2024 ACM / IEEE 17th International Conference on Cooperative and Human Aspects of Software Engineering

The whiteboard plays a crucial role in the day-to-day lives of software architects, as they frequently will organize meetings at the whiteboard to discuss a new architecture, some proposed changes to the architecture, a mismatch between the architecture and the code, and more. While much has been studied about software architects, the architectures they produce, and how they produce them, a detailed understanding of these whiteboards meetings is still lacking. In this paper, we contribute a mixed-methods study involving semi-structured interviews and a subsequent survey to understand the perceptions of software architects on whiteboard architecture meetings. We focus on five aspects: (1) why do they hold these meetings, what is the impact of the experience levels of the participants in these meetings, how do the architects document the meetings, what kinds of changes are made after the meetings have concluded and their results are moved to implementation, and what role do digital whiteboards plays? In studying these aspects, we identify 12 observations related to both technical aspects and social aspects of the meetings. These insights have implications for further research, offer concrete advice to practitioners, provide guidance for future tool design, and suggest ways of educating future software architects.

Microtask crowdsourcing is the practice of breaking down an overarching task to be performed into numerous, small, and quick microtasks that are distributed to an unknown, large set of workers. Microtask crowdsourcing has shown potential in other disciplines, but with only a handful of approaches explored to date in software engineering, its potential in our field remains unclear. In this paper, we explore how microtask crowdsourcing might serve as a means of fault localization. We particularly take a first step in assessing whether a crowd of workers can correctly locate known faults in a few lines of code (code fragments) taken from different open source projects. Through Mechanical Turk, we collected the answers of hundreds of workers to a pre-determined set of template questions applied to the code fragments, with a replication factor of twenty answers per question. Our findings show that a crowd can correctly distinguish questions that cover lines of code that contain a fault from those that do not. We also show that various filters can be applied to identify the most effective subcrowds. Our findings also presented serious limitations in terms of the proportion of lines of code selected for inspection and the cost to collect answers. We describe the design of our experiment, discuss the results, and provide an extensive analysis of different filters and their effects in terms of speed, cost, and effectiveness. We conclude with a discussion of limitations and possible future experiments toward more full-fledged fault localization on a large scale involving more complex faults., Comment: Large scale experiment, 28 pages, 18 figures, 32 tables, 129 references