10,639 research outputs found

    Non-Technical Individual Skills are Weakly Connected to the Maturity of Agile Practices

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    Context: Existing knowledge in agile software development suggests that individual competency (e.g. skills) is a critical success factor for agile projects. While assuming that technical skills are important for every kind of software development project, many researchers suggest that non-technical individual skills are especially important in agile software development. Objective: In this paper, we investigate whether non-technical individual skills can predict the use of agile practices. Method: Through creating a set of multiple linear regression models using a total of 113 participants from agile teams in six software development organizations from The Netherlands and Brazil, we analyzed the predictive power of non-technical individual skills in relation to agile practices. Results: The results show that there is surprisingly low power in using non-technical individual skills to predict (i.e. explain variance in) the mature use of agile practices in software development. Conclusions: Therefore, we conclude that looking at non-technical individual skills is not the optimal level of analysis when trying to understand, and explain, the mature use of agile practices in the software development context. We argue that it is more important to focus on the non-technical skills as a team-level capacity instead of assuring that all individuals possess such skills when understanding the use of the agile practices.Comment: 18 pages, 1 figur

    Technical Debt Prioritization: State of the Art. A Systematic Literature Review

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    Background. Software companies need to manage and refactor Technical Debt issues. Therefore, it is necessary to understand if and when refactoring Technical Debt should be prioritized with respect to developing features or fixing bugs. Objective. The goal of this study is to investigate the existing body of knowledge in software engineering to understand what Technical Debt prioritization approaches have been proposed in research and industry. Method. We conducted a Systematic Literature Review among 384 unique papers published until 2018, following a consolidated methodology applied in Software Engineering. We included 38 primary studies. Results. Different approaches have been proposed for Technical Debt prioritization, all having different goals and optimizing on different criteria. The proposed measures capture only a small part of the plethora of factors used to prioritize Technical Debt qualitatively in practice. We report an impact map of such factors. However, there is a lack of empirical and validated set of tools. Conclusion. We observed that technical Debt prioritization research is preliminary and there is no consensus on what are the important factors and how to measure them. Consequently, we cannot consider current research conclusive and in this paper, we outline different directions for necessary future investigations

    RePOR: Mimicking humans on refactoring tasks. Are we there yet?

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    Refactoring is a maintenance activity that aims to improve design quality while preserving the behavior of a system. Several (semi)automated approaches have been proposed to support developers in this maintenance activity, based on the correction of anti-patterns, which are `poor' solutions to recurring design problems. However, little quantitative evidence exists about the impact of automatically refactored code on program comprehension, and in which context automated refactoring can be as effective as manual refactoring. Leveraging RePOR, an automated refactoring approach based on partial order reduction techniques, we performed an empirical study to investigate whether automated refactoring code structure affects the understandability of systems during comprehension tasks. (1) We surveyed 80 developers, asking them to identify from a set of 20 refactoring changes if they were generated by developers or by a tool, and to rate the refactoring changes according to their design quality; (2) we asked 30 developers to complete code comprehension tasks on 10 systems that were refactored by either a freelancer or an automated refactoring tool. To make comparison fair, for a subset of refactoring actions that introduce new code entities, only synthetic identifiers were presented to practitioners. We measured developers' performance using the NASA task load index for their effort, the time that they spent performing the tasks, and their percentages of correct answers. Our findings, despite current technology limitations, show that it is reasonable to expect a refactoring tools to match developer code
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