51,053 research outputs found
Feature-Aware Verification
A software product line is a set of software products that are distinguished
in terms of features (i.e., end-user--visible units of behavior). Feature
interactions ---situations in which the combination of features leads to
emergent and possibly critical behavior--- are a major source of failures in
software product lines. We explore how feature-aware verification can improve
the automatic detection of feature interactions in software product lines.
Feature-aware verification uses product-line verification techniques and
supports the specification of feature properties along with the features in
separate and composable units. It integrates the technique of variability
encoding to verify a product line without generating and checking a possibly
exponential number of feature combinations. We developed the tool suite
SPLverifier for feature-aware verification, which is based on standard
model-checking technology. We applied it to an e-mail system that incorporates
domain knowledge of AT&T. We found that feature interactions can be detected
automatically based on specifications that have only feature-local knowledge,
and that variability encoding significantly improves the verification
performance when proving the absence of interactions.Comment: 12 pages, 9 figures, 1 tabl
Analysis of Feature Models Using Alloy: A Survey
Feature Models (FMs) are a mechanism to model variability among a family of
closely related software products, i.e. a software product line (SPL). Analysis
of FMs using formal methods can reveal defects in the specification such as
inconsistencies that cause the product line to have no valid products. A
popular framework used in research for FM analysis is Alloy, a light-weight
formal modeling notation equipped with an efficient model finder. Several works
in the literature have proposed different strategies to encode and analyze FMs
using Alloy. However, there is little discussion on the relative merits of each
proposal, making it difficult to select the most suitable encoding for a
specific analysis need. In this paper, we describe and compare those strategies
according to various criteria such as the expressivity of the FM notation or
the efficiency of the analysis. This survey is the first comparative study of
research targeted towards using Alloy for FM analysis. This review aims to
identify all the best practices on the use of Alloy, as a part of a framework
for the automated extraction and analysis of rich FMs from natural language
requirement specifications.Comment: In Proceedings FMSPLE 2016, arXiv:1603.0857
Potential Errors and Test Assessment in Software Product Line Engineering
Software product lines (SPL) are a method for the development of variant-rich
software systems. Compared to non-variable systems, testing SPLs is extensive
due to an increasingly amount of possible products. Different approaches exist
for testing SPLs, but there is less research for assessing the quality of these
tests by means of error detection capability. Such test assessment is based on
error injection into correct version of the system under test. However to our
knowledge, potential errors in SPL engineering have never been systematically
identified before. This article presents an overview over existing paradigms
for specifying software product lines and the errors that can occur during the
respective specification processes. For assessment of test quality, we leverage
mutation testing techniques to SPL engineering and implement the identified
errors as mutation operators. This allows us to run existing tests against
defective products for the purpose of test assessment. From the results, we
draw conclusions about the error-proneness of the surveyed SPL design paradigms
and how quality of SPL tests can be improved.Comment: In Proceedings MBT 2015, arXiv:1504.0192
Context for goal-level product line derivation
Product line engineering aims at developing a family of products and facilitating the derivation of product variants from it. Context can be a main factor in determining what products to derive. Yet, there is gap in incorporating context with variability models. We advocate that, in the first place, variability originates from human intentions and choices even before software systems are constructed, and context influences variability at this intentional level before the functional one. Thus, we propose to analyze variability at an early phase of analysis adopting the intentional ontology of goal models, and studying how context can influence such variability. Below we present a classification of variation points on goal models, analyze their relation with context, and show the process of constructing and maintaining the models. Our approach is illustrated with an example of a smarthome for people with dementia problems. 1
Prototyping Formal System Models with Active Objects
We propose active object languages as a development tool for formal system
models of distributed systems. Additionally to a formalization based on a term
rewriting system, we use established Software Engineering concepts, including
software product lines and object orientation that come with extensive tool
support. We illustrate our modeling approach by prototyping a weak memory
model. The resulting executable model is modular and has clear interfaces
between communicating participants through object-oriented modeling.
Relaxations of the basic memory model are expressed as self-contained variants
of a software product line. As a modeling language we use the formal active
object language ABS which comes with an extensive tool set. This permits rapid
formalization of core ideas, early validity checks in terms of formal invariant
proofs, and debugging support by executing test runs. Hence, our approach
supports the prototyping of formal system models with early feedback.Comment: In Proceedings ICE 2018, arXiv:1810.0205
Automated analysis of feature models: Quo vadis?
Feature models have been used since the 90's to describe software product lines as a way of reusing common parts in a family of software systems. In 2010, a systematic literature review was published summarizing the advances and settling the basis of the area of Automated Analysis of Feature Models (AAFM). From then on, different studies have applied the AAFM in different domains. In this paper, we provide an overview of the evolution of this field since 2010 by performing a systematic mapping study considering 423 primary sources. We found six different variability facets where the AAFM is being applied that define the tendencies: product configuration and derivation; testing and evolution; reverse engineering; multi-model variability-analysis; variability modelling and variability-intensive systems. We also confirmed that there is a lack of industrial evidence in most of the cases. Finally, we present where and when the papers have been published and who are the authors and institutions that are contributing to the field. We observed that the maturity is proven by the increment in the number of journals published along the years as well as the diversity of conferences and workshops where papers are published. We also suggest some synergies with other areas such as cloud or mobile computing among others that can motivate further research in the future.Ministerio de Economía y Competitividad TIN2015-70560-RJunta de Andalucía TIC-186
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