物件導向軟體之架構(Object-Oriented Software Construction)探討

Aspect] is a simple and practical aspect-oriented extension to Java With just a few new constructs, AspectJ provides support for modular implementation of a range of crosscutting concerns. In AspectJ's dynamic join point model, join points are well-defined points in the execution of the program; pointcuts are collections of join points; advice are special method-like constructs that can be attached to pointcuts; and aspects are modular units of crosscutting implementation, comprising pointcuts, advice, and ordinary Java member declarations. AspectJ code is compiled into standard Java bytecode. Simple extensions to existing Java development environments make it possible to browse the crosscutting structure of aspects in the same kind of way as one browses the inheritance structure of classes. Several examples show that AspectJ is powerful, and that programs written using it are easy to understand.

An overview of AspectJ

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Feature-Oriented Software Product Lines

Software-product-line engineering has gained considerable momentum in recent years, both in industry and in academia. A software product line is a family of software products that share a common set of features. Software product lines challenge traditional analysis techniques, such as type checking, model checking, and theorem proving, in their quest of ensuring correctness and reliability of software. Simply creating and analyzing all products of a product line is usually not feasible, due to the potentially exponential number of valid feature combinations. Recently, researchers began to develop analysis techniques that take the distinguishing properties of software product lines into account, for example, by checking feature-related code in isolation or by exploiting variability information during analysis. The emerging field of product-line analyses is both broad and diverse, so it is difficult for researchers and practitioners to understand their similarities and differences. We propose a classification of product-line analyses to enable systematic research and application. Based on our insights with classifying and comparing a corpus of 123 research articles, we develop a research agenda to guide future research on product-line analyses.

https://www.infosun.fim.uni-passau.de/publications/docs/TAK14.pdf

A Classification and Survey of Analysis Strategies for Software Product Lines

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Software Product Line Engineering Foundations Principles And Techniques

Product-line technology is increasingly used in mission-critical and safety-critical applications. Hence, researchers are developing verification approaches that follow different strategies to cope with the specific properties of product lines. While the research community is discussing the mutual strengths and weaknesses of the different strategies - mostly at a conceptual level - there is a lack of evidence in terms of case studies, tool implementations, and experiments. We have collected and prepared six product lines as subject systems for experimentation. Furthermore, we have developed a model-checking tool chain for C-based and Java-based product lines, called SPLverifier, which we use to compare sample-based and family-based strategies with regard to verification performance and the ability to find defects. Based on the experimental results and an analytical model, we revisit the discussion of the strengths and weaknesses of product-line-verification strategies.

/pdf/strategies-for-product-line-verification-case-studies-and-5avslsdy4o.pdf

Strategies for product-line verification: case studies and experiments

The advent of variability management and generator technology enables users to derive individual variants from a variable code base based on a selection of desired configuration options. This approach gives rise to the generation of possibly billions of variants that, however, cannot be efficiently analyzed for errors with classic analysis techniques. To address this issue, researchers and practitioners usually apply sampling heuristics. While sampling reduces the analysis effort significantly, the information obtained is necessarily incomplete and it is unknown whether sampling heuristics scale to billions of variants. Recently, researchers have begun to develop variability-aware analyses that analyze the variable code base directly exploiting the similarities among individual variants to reduce analysis effort. However, while being promising, so far, variability-aware analyses have been applied mostly only to small academic systems. To learn about the mutual strengths and weaknesses of variability-aware and sampling-based analyses of software systems, we compared the two strategies by means of two concrete analysis implementations (type checking and liveness analysis), applied them to three subject systems: Busybox, the x86 Linux kernel, and OpenSSL. Our key finding is that variability-aware analysis outperforms most sampling heuristics with respect to analysis time while preserving completeness.

/pdf/scalable-analysis-of-variable-software-3rkyb9fsoc.pdf

Scalable analysis of variable software

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 local knowledge.

/pdf/detection-of-feature-interactions-using-feature-aware-1nagui5k0c.pdf

Detection of feature interactions using feature-aware verification

https://www.infosun.fim.uni-passau.de/publications/docs/ART+13.pdf

Feature-interaction detection based on feature-based specifications

We investigate how to execute a unit test for all products of a product line without generating each product in isolation in a brute-force fashion. Learning from variability-aware analyses, we (a) design and implement a variability-aware interpreter and, alternatively, (b) reencode variability of the product line to simulate the test cases with a model checker. The interpreter internally reasons about variability, executing paths not affected by variability only once for the whole product line. The model checker achieves similar results by reusing powerful off-the-shelf analyses. We experimented with a prototype implementation for each strategy. We compare both strategies and discuss trade-offs and future directions. In the long run, we aim at finding an efficient testing approach that can be applied to entire product lines with millions of products.

/pdf/toward-variability-aware-testing-48e3ala6x8.pdf

Alexander von Rhein

Papers

Strategies for product-line verification: case studies and experiments

Scalable analysis of variable software

Detection of feature interactions using feature-aware verification

Feature-interaction detection based on feature-based specifications

Toward variability-aware testing