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Register a new userSignificance of the teamwork in agile software engineering
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M. Rizwan Jameel Qureshi Dr.
Publication date
2014
fields
Informatics Engineering
and research's language is
English
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A Software Engineering project depends significantly on team performance, as does any activity that involves human interaction. In the last years, the traditional perspective on software development is changing and agile methods have received considerable attention. Among other attributes, the ageists claim that fostering creativity is one of the keys to response to common problems and challenges of software development today. The development of new software products requires the generation of novel and useful ideas. It is a conceptual framework introduced in the Agile Manifesto in 2001. This paper is written in support of agile practices in terms of significance of teamwork for the success of software projects. Survey is used as a research method to know the significance of teamwork.
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A key goal of empirical research in software engineering is to assess practical significance, which answers whether the observed effects of some compared treatments show a relevant difference in practice in realistic scenarios. Even though plenty of standard techniques exist to assess statistical significance, connecting it to practical significance is not straightforward or routinely done; indeed, only a few empirical studies in software engineering assess practical significance in a principled and systematic way. In this paper, we argue that Bayesian data analysis provides suitable tools to assess practical significance rigorously. We demonstrate our claims in a case study comparing different test techniques. The case studys data was previously analyzed (Afzal et al., 2015) using standard techniques focusing on statistical significance. Here, we build a multilevel model of the same data, which we fit and validate using Bayesian techniques. Our method is to apply cumulative prospect theory on top of the statistical model to quantitatively connect our statistical analysis output to a practically meaningful context. This is then the basis both for assessing and arguing for practical significance. Our study demonstrates that Bayesian analysis provides a technically rigorous yet practical framework for empirical software engineering. A substantial side effect is that any uncertainty in the underlying data will be propagated through the statistical model, and its effects on practical significance are made clear. Thus, in combination with cumulative prospect theory, Bayesian analysis supports seamlessly assessing practical significance in an empirical software engineering context, thus potentially clarifying and extending the relevance of research for practitioners.
In this paper we introduce the notion of Modal Software Engineering: automatically turning sequential, deterministic programs into semantically equivalent programs efficiently operating on inputs coming from multiple overlapping worlds. We are drawing an analogy between modal logics, and software application domains where multiple sets of inputs (multiple worlds) need to be processed efficiently. Typically those sets highly overlap, so processing them independently would involve a lot of redundancy, resulting in lower performance, and in many cases intractability. Three application domains are presented: reasoning about feature-based variability of Software Product Lines (SPLs), probabilistic programming, and approximate programming.
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.
Bellwether effect refers to the existence of exemplary projects (called the Bellwether) within a historical dataset to be used for improved prediction performance. Recent studies have shown an implicit assumption of using recently completed projects (referred to as moving window) for improved prediction accuracy. In this paper, we investigate the Bellwether effect on software effort estimation accuracy using moving windows. The existence of the Bellwether was empirically proven based on six postulations. We apply statistical stratification and Markov chain methodology to select the Bellwether moving window. The resulting Bellwether moving window is used to predict the software effort of a new project. Empirical results show that Bellwether effect exist in chronological datasets with a set of exemplary and recently completed projects representing the Bellwether moving window. Result from this study has shown that the use of Bellwether moving window with the Gaussian weighting function significantly improve the prediction accuracy.
Industry in all sectors is experiencing a profound digital transformation that puts software at the core of their businesses. In order to react to continuously changing user requirements and dynamic markets, companies need to build robust workflows that allow them to increase their agility in order to remain competitive. This increasingly rapid transformation, especially in domains like IoT or Cloud computing, poses significant challenges to guarantee high quality software, since dynamism and agile short-term planning reduce the ability to detect and manage risks. In this paper, we describe the main challenges related to managing risk in agile software development, building on the experience of more than 20 agile coaches operating continuously for 15 years with hundreds of teams in industries in all sectors. We also propose a framework to manage risks that considers those challenges and supports collaboration, agility, and continuous development. An implementation of that framework is then described in a tool that handles risks and mitigation actions associated with the development of multi-cloud applications. The methodology and the tool have been validated by a team of evaluators that were asked to consider its use in developing an urban smart mobility service and an airline flight scheduling system.
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