Mining features from the object-oriented source code of software variants by combining lexical and structural similarity

Software product line (SPL) architecture facilitates systematic reuse to serve specic feature requests of new customers. Our work deals with the adoption of SPL architecture in an existing legacy system. In this case, the extractive approach of SPL adoption turned out to be the most viable method, where the system is redesigned keeping variants within the same code base. The analysis of the feature structure is a crucial point in this process as it involves both domain experts working at a higher level of abstraction and developers working directly on the program code. In this work, we propose an automatic method to extract feature-to-program connections starting from a very high level set of features provided by domain experts and existing program code. The extraction is performed by combining and further processing call graph information on the code with textual similarity between code and high level features. The context of our work is an industrial SPL adoption project of a large scale logistical information system written in an 4G language, Magic. We demonstrate the benets of the combined method and its use by dierent stakeholders in this project.

show abstract

“…Similar approaches have been previously proposed for traditional object oriented systems, e.g. by Al-msie'deen et al [10].…”

Section: Introductionmentioning

confidence: 83%

“…In our previous work [11] we already presented our LSI-based approach to feature extraction. LSI is already known to be capable of producing quality results combined with structural information [10]. Being a fourth generation language, Magic does not completely follow the structure of a traditional programming language.…”

Section: Feature Extraction Approachmentioning

confidence: 99%

Supporting Product Line Adoption by Combining Syntactic and Textual Feature Extraction

Kicsi

Vidács

Csuvik

et al. 2018

New Opportunities for Software Reuse

View full text Add to dashboard Cite

show abstract

“…Eyal-Salman et al [6] use LSI for recovering traceability link between features and source code with about 80% success rate, but experiments are done only for a small set of features of a simple java program. IR-based solution for feature extraction is combined with structural information in the work of Almsie'deen et al [23]. This is a promising direction and in case of Magic applications call dependencies are also planned to be used for detailed feature analysis.…”

Section: Related Workmentioning

confidence: 99%

Information retrieval based feature analysis for product line adoption in 4GL systems

Kicsi

Vidács

Beszedes

et al. 2017

2017 17th International Conference on Computational Science and Its Applications (ICCSA)

View full text Add to dashboard Cite

New customers often require custom features of a successfully marketed product. As the number of variants grow, new challenges arise in the maintenance and evolution activities. Software product line (SPL) architecture is a timely answer to these challenges. The SPL adoption however is a large one time investment that affects both technical and organizational issues. From the program code point of view, the extractive approach is appropriate when there are already several product variants. Analyzing the feature structure, the differences and commonalities of the variants lead to the new common architecture. In this work in progress paper we report initial experiments of feature extraction from a set of product variants written in the Magic fourth generation language (4GL). Since existing approaches are mostly designed for mainstream languages, we adapted and reused reverse engineering approaches to the 4GL environment. We followed a semi-automatic feature extraction method, where the higher level features are provided by domain experts. These features are then linked to the internal structure of Magic applications using a textual similarity (IR-based) method. We demonstrate the feasibility of 4GL feature extraction method and validate it on two variants of a real life logistical system each consisting of more than 2000 Magic programs.

show abstract

“…The artefacts considered as input are product descriptions [12,14,18,19], requirements [6,10,27,36], configurators [1], source code [5,26,33,39], or models [23]. The objectives are usually to locate features or configuration options, to formalize logical relations, and possibly to re-engineer a system with a generative, variability-based approach.…”

Section: Reverse Engineering Variabilitymentioning

confidence: 99%

On the Variability Secrets of an Online Video Generator

Bécan

Acher

Jézéquel

et al. 2015

Proceedings of the Ninth International Workshop on Variability Modelling of Software-Intensive Systems

View full text Add to dashboard Cite

International audienceWe relate an original experience concerning a popular online video service that offers to generate variants of an humorous video. To further the understanding of the generator, we have reverse engineered its general behavior, architecture, as well as its variation points and its configuration space. The reverse engineering also allows us to create a new generator and online configurator that proposes 18 variation points – instead of only 3 as in the original generator. We explain why and how we have collaborated and are collaborating with the original creators of the video generator. We also highlight how our reverse engineering work represents a threat to the original service and call for further investigating variability-aware security mechanisms

show abstract

Mining features from the object-oriented source code of software variants by combining lexical and structural similarity

Cited by 26 publications

References 10 publications

Supporting Product Line Adoption by Combining Syntactic and Textual Feature Extraction

Supporting Product Line Adoption by Combining Syntactic and Textual Feature Extraction

Information retrieval based feature analysis for product line adoption in 4GL systems

On the Variability Secrets of an Online Video Generator

Contact Info

Product

Resources

About