Analyzing the Instability of the Core Components of Software Projects

Guarda, Daniela; Tortorella, Maria

doi:10.24251/hicss.2018.707

Cited by 4 publications

(3 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The authors analyze the stability of changes in classes using change proneness measures (i.e., a priori estimation) and instability measures (i.e., a posteriori measures). An extension of the aforementioned work has been done by Arvanitou et al (2017), where the authors propose a method for assessing change proneness in classes due to evolving requirements, bug fixing, and ripple effect.…”

Section: Software Instabilitymentioning

confidence: 99%

“…Regarding software instability, Santos et al (2017) investigate the instability based on afferent and efferent coupling metrics in OSS projects, and performed and statistical analysis of the results observing that 48% of software product had a high instability. In addition, Aversano et al (2018) analyzed the instability of architecture core components across releases and they defined instability metrics based on the packages that are added, removed, or changed. Salama and Bahsoon (2017) study the architectural stability of self-adaptive systems to achieve stable adaptations, and where instability can be considered an indicator of the sustainability of the system and architecture as well.…”

Section: Software Instabilitymentioning

confidence: 99%

“…Salama and Bahsoon (2017) study the architectural stability of self-adaptive systems to achieve stable adaptations, and where instability can be considered an indicator of the sustainability of the system and architecture as well. Although other works, such as Ampatzoglou et al (2015) and Arvanitou et al (2017), highlight that instability and change proneness measures a clear effect on the stability of a system as an indicator of its sustainability, only seminal works from Carrillo et al (2015) and Venters et al (2018) suggest metrics to estimate the sustainability of architectural decisions. Sas et al (2019) investigate in more recent works the evolution of the instability in architectural smells across 524 versions in 14 open-source projects using the Arcan tool.…”

Section: Software Instabilitymentioning

confidence: 99%

See 2 more Smart Citations

Can instability variations warn developers when open-source projects boost?

Capilla,

Salamanca,

Valdezate

et al. 2024

Empir Software Eng

View full text Add to dashboard Cite

Although architecture instability has been studied and measured using a variety of metrics, a deeper analysis of which project parts are less stable and how such instability varies over time is still needed. While having more information on architecture instability is, in general, useful for any software development project, it is especially important in Open Source Software (OSS) projects where the supervision of the development process is more difficult to achieve. In particular, we are interested when OSS projects grow from a small controlled environment (i.e., the cathedral phase) to a community-driven project (i.e., the bazaar phase). In such a transition, the project often explodes in terms of software size and number of contributing developers. Hence, the complexity of the newly added features, and the frequency of the commits and files modified may cause significant variations of the instability of the structure of the classes and packages. Consequently, in this article we analyze the instability in OSS projects, especially during that sensitive phase where they become community-driven. Our results show that instability metrics can be easily obtained in such type of transitions. We also observed from our case studies that instability metrics can help finding out the balance between adding new functionality and performing refactoring. As a conclusions we state that instability metrics offer relevant information in the transition phase from the cathedral to the bazaar.

show abstract

Section: Software Instabilitymentioning

confidence: 99%