111,205 research outputs found
An assessment of search-based techniques for reverse engineering feature models
Successful software evolves from a single system by adding and changing functionality to keep up with users’ demands and to cater to their similar and different requirements. Nowadays it is a common practice to offer a system in many variants such as community, professional, or academic editions. Each variant provides different functionality described in terms of features. Software Product Line Engineering (SPLE) is an effective software development paradigm for this scenario. At the core of SPLE is variability modelling whose goal is to represent the combinations of features that distinguish the system variants using feature models, the de facto standard for such task. As SPLE practices are becoming more pervasive, reverse engineering feature models from the feature descriptions of each individual variant has become an active research subject. In this paper we evaluated, for this reverse engineering task, three standard search based techniques (evolutionary algorithms, hill climbing, and random search) with two objective functions on 74 SPLs. We compared their performance using precision and recall, and found a clear trade-off between these two metrics which we further reified into a third objective function based on Fβ, an information retrieval measure, that showed a clear performance improvement. We believe that this work sheds light on the great potential of search-based techniques for SPLE tasks.Ministerio de Economía y Competitividad TIN2012-32273Junta de Andalucía TIC-186
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
Combining Bayesian Approaches and Evolutionary Techniques for the Inference of Breast Cancer Networks
Gene and protein networks are very important to model complex large-scale
systems in molecular biology. Inferring or reverseengineering such networks can
be defined as the process of identifying gene/protein interactions from
experimental data through computational analysis. However, this task is
typically complicated by the enormously large scale of the unknowns in a rather
small sample size. Furthermore, when the goal is to study causal relationships
within the network, tools capable of overcoming the limitations of correlation
networks are required. In this work, we make use of Bayesian Graphical Models
to attach this problem and, specifically, we perform a comparative study of
different state-of-the-art heuristics, analyzing their performance in inferring
the structure of the Bayesian Network from breast cancer data
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A computer-based product classification and component detection for demanufacturing processes
This is an Author's Accepted Manuscript of an article published in International Journal of Computer Integrated
Manufacturing, 24(10), 900-914, 2011 [copyright Taylor & Francis], available online at:
http://www.tandfonline.com/10.1080/0951192X.2011.579169.The aim of this paper is to propose a novel computer-based product classification, component detection and tracking for demanufacturing and disassembly process. This is achieved by introducing a series of automated and sequential product scanning, component identification, image analysis and sorting – leading to the development of a bill of material (BOM). The produced BOM can then be associated with the relevant disassembly/demanufacture proviso. The proposed integrated image sorting and product classification (ISPC) approach can be considered as a step forward in automation of demanufacturing activities. The ISPC model proposed in this paper utilises and builds on the state-of-the-art technology and current body of research in computer-integrated demanufacturing and remanufacturing (CIDR). An appraisal of the latest research material and the factors that inhibit CIDR methods inpractice are presented. A novel solution for the integration of imaging and material identification techniques toovercome some of the existing shortcomings of automated recycling processes is proposed in this paper. The proposed product scanning and component detection ISPC software consists of four distinct models: the repertory database, the search engine, the product-attributes updater and the image sorting and classification algorithm. The software framework that integrates the four components is presented in this paper. Finally, an overall assessment of applying ISPC at various stages of CIDR processes concludes the article.University of Ibadan MacArthur Foundation Gran
Similarity measures for mid-surface quality evaluation
Mid-surface models are widely used in engineering analysis to simplify the analysis of thin-walled parts, but it can be difficult to ensure that the mid-surface model is representative of the solid part from which it was generated. This paper proposes two similarity measures that can be used to evaluate the quality of a mid-surface model by comparing it to a solid model of the same part. Two similarity measures are proposed; firstly a geometric similarity evaluation technique based on the Hausdorff distance and secondly a topological similarity evaluation method which uses geometry graph attributes as the basis for comparison. Both measures are able to provide local and global similarity evaluation for the models. The proposed methods have been implemented in a software demonstrator and tested on a selection of representative models. They have been found to be effective for identifying geometric and topological errors in mid-surface models and are applicable to a wide range of practical thin-walled designs
Embedding Spatial Software Visualization in the IDE: an Exploratory Study
Software visualization can be of great use for understanding and exploring a
software system in an intuitive manner. Spatial representation of software is a
promising approach of increasing interest. However, little is known about how
developers interact with spatial visualizations that are embedded in the IDE.
In this paper, we present a pilot study that explores the use of Software
Cartography for program comprehension of an unknown system. We investigated
whether developers establish a spatial memory of the system, whether clustering
by topic offers a sound base layout, and how developers interact with maps. We
report our results in the form of observations, hypotheses, and implications.
Key findings are a) that developers made good use of the map to inspect search
results and call graphs, and b) that developers found the base layout
surprising and often confusing. We conclude with concrete advice for the design
of embedded software maps.Comment: To appear in proceedings of SOFTVIS 2010 conferenc
AI and OR in management of operations: history and trends
The last decade has seen a considerable growth in the use of Artificial Intelligence (AI) for operations management with the aim of finding solutions to problems that are increasing in complexity and scale. This paper begins by setting the context for the survey through a historical perspective of OR and AI. An extensive survey of applications of AI techniques for operations management, covering a total of over 1200 papers published from 1995 to 2004 is then presented. The survey utilizes Elsevier's ScienceDirect database as a source. Hence, the survey may not cover all the relevant journals but includes a sufficiently wide range of publications to make it representative of the research in the field. The papers are categorized into four areas of operations management: (a) design, (b) scheduling, (c) process planning and control and (d) quality, maintenance and fault diagnosis. Each of the four areas is categorized in terms of the AI techniques used: genetic algorithms, case-based reasoning, knowledge-based systems, fuzzy logic and hybrid techniques. The trends over the last decade are identified, discussed with respect to expected trends and directions for future work suggested
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