PRECOG: a Descriptive Cognitive Model for predicting usability issues in a Low Code Development Platform

Low-code development platforms (LCDPs) address the need for increased productivity in software development. There is still, however, a considerable number of relevant gaps in current knowledge about how people reason during programming and development tasks, as well as a need for new tools to fill these gaps.

Low-code development platforms (LCDPs) address the need for increased productivity in software development. By raising the abstraction level at which software is developed, LCDPs automate low-level and routine development tasks, effectively contributing to solve the problem of global shortage of professional software developers. According to MarketsandMarkets (2020), the Global LCDPs market is projected to grow from €11,2 billion in 2020 to €38,8 billion by 2025. At the same time, they lower the entry barrier to software development. As these low-level tasks become automated, developers are not required to carry them out (or even to know how to carry them out). Low-level technical details are effectively hidden by the platform.

The challenges of Low-code Development Platforms

In any case, the users’ prior knowledge plays a relevant role in the learning and use of a platform (Dijkstra, 1982). In the case of LCDPs, there is the double challenge of supporting users with little or no knowledge of programming, while also supporting expert programmers. Indeed, understanding individual differences and expectations, and identifying the sources of variation among different users will help this type of platform to be more broadly adopted (Blackwell, 2017). Since low-code development platforms aim at reducing the learning burden while providing powerful tools to address a wide range of application domains, a trade-off must be established between the scope of application and the learning costs of the platforms and their languages. This necessarily implies building an understanding of how different types of users approach the platforms.

PRECOG: a model to predict usability issues in LCDPs

As a contribution to this long-term goal, we have been looking at the difficulties faced by potential programmers with different expectations and academic backgrounds when using LCDPs. To achieve this, we developed PRECOG (see Figure 1), a descriptive cognitive model designed to help predict usability issues in LCDPs.

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Figure 1. PRECOG — low-code development Platform descRiptivE COGnitive model (adapted from Silva et al., 2020)
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Figure 2. Descriptors for each level of Criticality
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Figure 3. Frequency/Criticality matrix — the cell at the intersection of the Frequency’s value row with the Criticality’s value column indicates the Pure Risk (adapted from Amir-Heidari and Ebrahemzadih, 2015).

Application of the PRECOG

We have successfully applied PRECOG to a relevant use case, which allowed us to validate the viability of the proposed approach, as a way of uncovering mismatches between user’s mental models of a development task and the LCDPs’ support to solve the task. Twenty programmers (10 experts, 10 novices) participated in the analysis of a market leading LCDP. The application of the PRECOG methodology was followed by user tests for direct observation of programmers interaction with the platform.

Previous knowledge as predictor of interaction difficulties

Low-code development platforms have the potential to dramatically change how software is developed, making it possible, at least for particular domains, for someone without a formal education in computer science to develop quality software, and for experienced developers to significantly speed up the development process.

Short bio of authors

João Abril de Abreu is a Program Manager at OutSystems, in charge of R&D outreach and product innovation. João holds a PhD. degree in Computer Science from the University of Leicester (UK, 2010).

References

Blackwell, A. F. (2017). End-user developers–what are they like? In New perspectives in end-user development (pp. 121–135). Springer International Publishing. https://doi.org/10.1007/978-3-319-60291-2_6

INESC TEC is a private non-profit research institution, dedicated to scientific research and technological development.

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