Concepts, Operations, and Feasibility of a Projection-Based Variation Control System

Stănciulescu, Ștefan; Berger, Thorsten; Walkingshaw, Eric; Wąsowski, Andrzej

doi:10.1109/icsme.2016.88

Cited by 31 publications

(17 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Interestingly, these parameters represent both variations and versions, since some of them map to pre-processor macros, and the version-control system directly supports checking out variants. As such, variability and version management are to some extent unified (like in variation control systems Linsbauer et al 2017;Stanciulescu et al 2016;Berger et al 2019a).…”

Section: Automotive Firmware Use Casementioning

confidence: 99%

The state of adoption and the challenges of systematic variability management in industry

et al. 2020

Self Cite

View full text Add to dashboard Cite

Handling large-scale software variability is still a challenge for many organizations. After decades of research on variability management concepts, many industrial organizations have introduced techniques known from research, but still lament that pure textbook approaches are not applicable or efficient. For instance, software product line engineering-an approach to systematically develop portfolios of products-is difficult to adopt given the high upfront investments; and even when adopted, organizations are challenged by evolving their complex product lines. Consequently, the research community now mainly focuses on reengineering and evolution techniques for product lines; yet, understanding the current state of adoption and the industrial challenges for organizations is necessary to conceive effective techniques. In this multiple-case study, we analyze the current adoption of variability management techniques in twelve medium-to large-scale industrial cases in domains such as automotive, aerospace or railway systems. We identify the current state of variability management, emphasizing the techniques and concepts they adopted. We elicit the needs and challenges expressed for these cases, triangulated with results from a literature review. We believe our results help to understand the current state of adoption and shed light on gaps to address in industrial practice.

show abstract

Section: Automotive Firmware Use Casementioning

confidence: 99%

The state of adoption and the challenges of systematic variability management in industry

et al. 2020

Self Cite

View full text Add to dashboard Cite

show abstract

“…As a project grows and matures, the contributors' goals or perspectives may diverge, and some may want to take the project in a different direction. If taken to the extreme, hard forks can be used for variant management, in which multiple related but different projects originating from the same source are maintained separately [3,13,14,45]. • Governance disputes.…”

Section: Motivations For Forkingmentioning

confidence: 99%

“…Social coding platforms contain both kinds of forks and they are not always easy to distinguish. Diffusion of efforts and fragmentation of communities, as always feared in discussions of hard forks, can be observed also on GitHub: Many secondary forks (i.e., forks of forks) contribute to other forks, but not to the original repository, and forks slowly drift apart [16,45]. A key question is, thus, whether the popularity of social forking encourages also hard forks and causes similar fragmentation and sustainability challenges feared in the past.…”

Section: Pros and Cons Of Hard Forksmentioning

confidence: 99%

“…Recent academic tools for improved monitoring [33,52] or cross-fork change migration [35,37] are potentially promising but are not yet accessible easily to practitioners. Also more experimental ideas about virtual product-line platforms that unify development of multiple variants of a project [3,14,29,40,45] may provide inspiration for maintaining and coordinating hard forks, though they typically do not currently support the distributed nature of development with competing hard forks. A technical solution could solve the specificity-generality dilemma (cf.…”

Section: Interactions Between Fork and Upstream Repositorymentioning

confidence: 99%

See 1 more Smart Citation

How has forking changed in the last 20 years?

Zhou

Vasilescu

Kästner

2020

Proceedings of the ACM/IEEE 42nd International Conference on Software Engineering

View full text Add to dashboard Cite

The notion of forking has changed with the rise of distributed version control systems and social coding environments, like GitHub. Traditionally forking refers to splitting off an independent development branch (which we call hard forks); research on hard forks, conducted mostly in pre-GitHub days showed that hard forks were often seen critical as they may fragment a community. Today, in social coding environments, open-source developers are encouraged to fork a project in order to contribute to the community (which we call social forks), which may have also influenced perceptions and practices around hard forks. To revisit hard forks, we identify, study, and classify 15,306 hard forks on GitHub and interview 18 owners of hard forks or forked repositories. We find that, among others, hard forks often evolve out of social forks rather than being planned deliberately and that perception about hard forks have indeed changed dramatically, seeing them often as a positive noncompetitive alternative to the original project.

show abstract

“…Other applications of the choice calculus include the development of variational data structures [Meng et al 2017;Smeltzer and Erwig 2017;] to support variational program execution Erwig and Walkingshaw 2013;Nguyen et al 2014], and view-based editing of variational programs [Stănciulescu et al 2016;Walkingshaw and Ostermann 2014].…”

Section: Variational Typingmentioning

confidence: 99%

Migrating gradual types

Campora

Chen

Erwig

et al. 2017

Proc. ACM Program. Lang.

Self Cite

View full text Add to dashboard Cite

Gradual typing allows programs to enjoy the benefits of both static typing and dynamic typing. While it is often desirable to migrate a program from more dynamically-typed to more statically-typed or vice versa, gradual typing itself does not provide a way to facilitate this migration. This places the burden on programmers who have to manually add or remove type annotations. Besides the general challenge of adding type annotations to dynamically typed code, there are subtle interactions between these annotations in gradually typed code that exacerbate the situation. For example, to migrate a program to be as static as possible, in general, all possible combinations of adding or removing type annotations from parameters must be tried out and compared.In this paper, we address this problem by developing migrational typing, which efficiently types all possible ways of adding or removing type annotations from a gradually typed program. The typing result supports automatically migrating a program to be as static as possible, or introducing the least number of dynamic types necessary to remove a type error. The approach can be extended to support user-defined criteria about which annotations to modify. We have implemented migrational typing and evaluated it on large programs. The results show that migrational typing scales linearly with the size of the program and takes only 2ś4 times longer than plain gradual typing.

show abstract

Concepts, Operations, and Feasibility of a Projection-Based Variation Control System

Cited by 31 publications

References 21 publications

The state of adoption and the challenges of systematic variability management in industry

The state of adoption and the challenges of systematic variability management in industry

How has forking changed in the last 20 years?

Migrating gradual types

Contact Info

Product

Resources

About