Name
Abstract | ||
|---|---|---|
Early visions of component-based software development have been realized, with software projects now composed mostly of other peoples code. However, the challenge of selecting the best components, with speed and confidence in the result, has only become more difficult. Previous work has focused on systematic approaches to component selection, but in continuous-
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and agile settings, the increase in confidence from being systematic is not worth the cost of delay. In this emerging ideas paper, we present early results on work to balance speed with confidence in component selection. Our idea is to define a scorecard for components based on high-level quality attribute indicators, project health measures, and a context-specific aggregation function for producing a single yes/no decision for integrators. We present preliminary results showing how this scorecard approach works on computer vision components. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/ICSA-C.2019.00023 | 2019 IEEE International Conference on Software Architecture Companion (ICSA-C) |
Keywords | Field | DocType |
continuous approach,component-based software development,software projects,peoples code,component selection,project health measures,computer vision components,context-specific aggregation function,high-level quality attribute indicators,component evaluation,component comparison | Data mining,Computer science | Conference |
ISBN | Citations | PageRank |
978-1-7281-1877-2 | 0 | 0.34 |
References | Authors | |
13 | 3 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Neil A. Ernst | 1 | 69 | 4.03 |
| Rick Kazman | 2 | 34 | 2.48 |
| Philip Bianco | 3 | 3 | 1.27 |