Subscribe to the gold package and get unlimited access to Shamra Academy
Register a new userAn Ontological Analysis of a Proposed Theory for Software Development
385
0
0.0
(
0
)
Added by
Stephen MacDonell
Publication date
2021
fields
Informatics Engineering
and research's language is
English
Ask ChatGPT about the research
No Arabic abstract
There is growing acknowledgement within the software engineering community that a theory of software development is needed to integrate the myriad methodologies that are currently popular, some of which are based on opposing perspectives. We have been developing such a theory for a number of years. In this paper, we overview our theory and report on a recent ontological analysis of the theory constructs. We suggest that, once fully developed, this theory, or one similar to it, may be applied to support situated software development, by providing an overarching model within which software initiatives might be categorised and understood. Such understanding would inevitably lead to greater predictability with respect to outcomes.
rate research
Read More
There is growing acknowledgement within the software engineering community that a theory of software development is needed to integrate the myriad methodologies that are currently popular, some of which are based on opposing perspectives. We have been developing such a theory for a number of years. In this position paper, we overview our theory along with progress made thus far. We suggest that, once fully developed, this theory, or one similar to it, may be applied to support situated software development, by providing an overarching model within which software initiatives might be categorised and understood. Such understanding would inevitably lead to greater predictability with respect to outcomes.
Software Heritage is the largest existing public archive of software source code and accompanying development history. It spans more than five billion unique source code files and one billion unique commits , coming from more than 80 million software projects. These software artifacts were retrieved from major collaborative development platforms (e.g., GitHub, GitLab) and package repositories (e.g., PyPI, Debian, NPM), and stored in a uniform representation linking together source code files, directories, commits, and full snapshots of version control systems (VCS) repositories as observed by Software Heritage during periodic crawls. This dataset is unique in terms of accessibility and scale, and allows to explore a number of research questions on the long tail of public software development, instead of solely focusing on most starred repositories as it often happens.
The complexity of software tasks and the uncertainty of crowd developer behaviors make it challenging to plan crowdsourced software development (CSD) projects. In a competitive crowdsourcing marketplace, competition for shared worker resources from multiple simultaneously open tasks adds another layer of uncertainty to the potential outcomes of software crowdsourcing. These factors lead to the need for supporting CSD managers with automated scheduling to improve the visibility and predictability of crowdsourcing processes and outcomes. To that end, this paper proposes an evolutionary algorithm-based task scheduling method for crowdsourced software development. The proposed evolutionary scheduling method uses a multiobjective genetic algorithm to recommend an optimal task start date. The method uses three fitness functions, based on project duration, task similarity, and task failure prediction, respectively. The task failure fitness function uses a neural network to predict the probability of task failure with respect to a specific task start date. The proposed method then recommends the best tasks start dates for the project as a whole and each individual task so as to achieve the lowest project failure ratio. Experimental results on 4 projects demonstrate that the proposed method has the potential to reduce project duration by a factor of 33-78%.
Objective: The purpose of this paper is to identify the largest cognitive challenges faced by novices developing software in teams. Method: Using grounded theory, we conducted an ethnographic study for two months following four ten person novice teams, consisting of computer science students, developing software systems. Result: This paper identifies version control and merge operations as the largest challenge faced by the novices. The literature studies reveal that little research appears to have been carried out in the area of version control from a user perspective. Limitations: A qualitative study on students is not applicable in all contexts, but the result is credible and grounded in data and substantiated by extant literature. Conclusion: We conclude that our findings motivate further research on cognitive perspectives to guide improvement of software engineering and its tools.
Developing sustainable scientific software for the needs of the scientific community requires expertise in both software engineering and domain science. This can be challenging due to the unique needs of scientific software, the insufficient resources for modern software engineering practices in the scientific community, and the complexity of evolving scientific contexts for developers. These difficulties can be reduced if scientists and developers collaborate. We present a case study wherein scientists from the SuperNova Early Warning System collaborated with software developers from the Scalable Cyberinfrastructure for Multi-Messenger Astrophysics project. The collaboration addressed the difficulties of scientific software development, but presented additional risks to each team. For the scientists, there was a concern of relying on external systems and lacking control in the development process. For the developers, there was a risk in supporting the needs of an user-group while maintaining core development. We mitigated these issues by utilizing an Agile Scrum framework to orchestrate the collaboration. This promoted communication and cooperation, ensuring that the scientists had an active role in development while allowing the developers to quickly evaluate and implement the scientists software requirements. While each system was still in an early stage, the collaboration provided benefits for each group: the scientists kick-started their development by using an existing platform, and the developers utilized the scientists use-case to improve their systems. This case study suggests that scientists and software developers can avoid some difficulties of scientific computing by collaborating and can address emergent concerns using Agile Scrum methods.
Log in to be able to interact and post comments
comments
Fetching comments
Sign in to be able to follow your search criteria
