Transitioning legacy assets to a product line architecture

Bayer, Joachim; Girard, Jean‐François; Wurthner, M.; DeBaud, Jean-Marc; Apel, Martin

doi:10.1145/318774.319262

Cited by 16 publications

(12 citation statements)

References 12 publications

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“…From a process and business perspective, researchers have developed approaches to reengineer existing systems into an integrated configurable system [7], [14], [49], [52]. These approaches include strategies to make decisions: when to mine, which assets to mine, and whom to involve.…”

Section: Related Workmentioning

confidence: 99%

Where Do Configuration Constraints Stem From? An Extraction Approach and an Empirical Study

Nadi

Berger

Kästner

et al. 2015

IIEEE Trans. Software Eng.

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Abstract-Highly configurable systems allow users to tailor software to specific needs. Valid combinations of configuration options are often restricted by intricate constraints. Describing options and constraints in a variability model allows reasoning about the supported configurations. To automate creating and verifying such models, we need to identify the origin of such constraints. We propose a static analysis approach, based on two rules, to extract configuration constraints from code. We apply it on four highly configurable systems to evaluate the accuracy of our approach and to determine which constraints are recoverable from the code. We find that our approach is highly accurate (93 % and 77 % respectively) and that we can recover 28 % of existing constraints. We complement our approach with a qualitative study to identify constraint sources, triangulating results from our automatic extraction, manual inspections, and interviews with 27 developers. We find that, apart from low-level implementation dependencies, configuration constraints enforce correct runtime behavior, improve users' configuration experience, and prevent corner cases. While the majority of constraints is extractable from code, our results indicate that creating a complete model requires further substantial domain knowledge and testing. Our results aim at supporting researchers and practitioners working on variability model engineering, evolution, and verification techniques.

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Section: Related Workmentioning

confidence: 99%

Where Do Configuration Constraints Stem From? An Extraction Approach and an Empirical Study

Nadi

Berger

Kästner

et al. 2015

IIEEE Trans. Software Eng.

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“…The decision depends on many influence factors, including many business and process considerations discussed elsewhere [5], [7], [27], [49], [55], [56]. Recently, She et al even explored extracting variability models from legacy code and other sources [54].…”

Section: Existing Support For Variability Miningmentioning

confidence: 99%

“…For example, if method declaration lock in our running example (target element) is annotated with feature locking whereas the corresponding method invocation in Line 7 (source element) is not annotated, a variant without feature locking would result in a method invocation that cannot be resolved. 5 Already when first experimenting with early versions of the type system over five years ago, we found using type errors for variability mining almost obvious. When annotating a code fragment, say method lock in Figure 1, with a feature, the type system immediately reports errors at all locations at which lock is invoked without the same feature annotation (Lines 7 and 12 in Fig.…”

Section: Type Systemmentioning

confidence: 99%

“…The process of migrating features from legacy applications to a product line is sometimes named asset mining [5], [7], [55], [56]. Whereas we focus on technical issues regarding locating, documenting, and extracting source code of a feature, previous work on asset mining focused mostly on process and business considerations: when to mine, which features to mine, or whom to involve.…”

Section: Related Workmentioning

confidence: 99%

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Variability Mining: Consistent Semi-automatic Detection of Product-Line Features

Kästner

Dreiling

Ostermann

2014

IIEEE Trans. Software Eng.

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Abstract-Software product line engineering is an efficient means to generate a set of tailored software products from a common implementation. However, adopting a product-line approach poses a major challenge and significant risks, since typically legacy code must be migrated toward a product line. Our aim is to lower the adoption barrier by providing semiautomatic tool support-called variability mining-to support developers in locating, documenting, and extracting implementations of product-line features from legacy code. Variability mining combines prior work on concern location, reverse engineering, and variability-aware type systems, but is tailored specifically for the use in product lines. Our work pursues three technical goals: (1) we provide a consistency indicator based on a variability-aware type system, (2) we mine features at a fine level of granularity, and (3) we exploit domain knowledge about the relationship between features when available. With a quantitative study, we demonstrate that variability mining can efficiently support developers in locating features.

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“…Bayer et al [19] propose the RE_MODEL method to integrate reengineering and product line activities in order to achieve a transition into product line architecture. A key element in the method is the blackboard, a work space which is shared for both activities that are done in parallel.…”

Section: Software Product Line Methodsmentioning

confidence: 99%