A convolutional neural-network model of human cochlear mechanics and filter tuning for real-time applications

Auditory models are commonly used as feature extractors for automatic speech-recognition systems or as front-ends for robotics, machine-hearing and hearing-aid applications. Although auditory models can capture the biophysical and nonlinear properties of human hearing in great detail, these biophysical models are computationally expensive and cannot be used in real-time applications. We present a hybrid approach where convolutional neural networks are combined with computational neuroscience to yield a real-time end-to-end model for human cochlear mechanics, including level-dependent filter tuning (CoNNear). The CoNNear model was trained on acoustic speech material and its performance and applicability were evaluated using (unseen) sound stimuli commonly employed in cochlear mechanics research. The CoNNear model accurately simulates human cochlear frequency selectivity and its dependence on sound intensity, an essential quality for robust speech intelligibility at negative speech-to-background-noise ratios. The CoNNear architecture is based on parallel and differentiable computations and has the power to achieve real-time human performance. These unique CoNNear features will enable the next generation of human-like machine-hearing applications

Modellbasiertes Regressionstesten von Varianten und Variantenversionen

The quality assurance of software product lines (SPL) achieved via testing is a crucial and challenging activity of SPL engineering. In general, the application of single-software testing techniques for SPL testing is not practical as it leads to the individual testing of a potentially vast number of variants. Testing each variant in isolation further results in redundant testing processes by means of redundant test-case executions due to the shared commonality. Existing techniques for SPL testing cope with those challenges, e.g., by identifying samples of variants to be tested. However, each variant is still tested separately without taking the explicit knowledge about the shared commonality and variability into account to reduce the overall testing effort. Furthermore, due to the increasing longevity of software systems, their development has to face software evolution. Hence, quality assurance has also to be ensured after SPL evolution by testing respective versions of variants. In this thesis, we tackle the challenges of testing redundancy as well as evolution by proposing a framework for model-based regression testing of evolving SPLs. The framework facilitates efficient incremental testing of variants and versions of variants by exploiting the commonality and reuse potential of test artifacts and test results. Our contribution is divided into three parts. First, we propose a test-modeling formalism capturing the variability and version information of evolving SPLs in an integrated fashion. The formalism builds the basis for automatic derivation of reusable test cases and for the application of change impact analysis to guide retest test selection. Second, we introduce two techniques for incremental change impact analysis to identify (1) changing execution dependencies to be retested between subsequently tested variants and versions of variants, and (2) the impact of an evolution step to the variant set in terms of modified, new and unchanged versions of variants. Third, we define a coverage-driven retest test selection based on a new retest coverage criterion that incorporates the results of the change impact analysis. The retest test selection facilitates the reduction of redundantly executed test cases during incremental testing of variants and versions of variants. The framework is prototypically implemented and evaluated by means of three evolving SPLs showing that it achieves a reduction of the overall effort for testing evolving SPLs.Testen ist ein wichtiger Bestandteil der Entwicklung von Softwareproduktlinien (SPL). Aufgrund der potentiell sehr großen Anzahl an Varianten einer SPL ist deren individueller Test im Allgemeinen nicht praktikabel und resultiert zudem in redundanten Testfallausführungen, die durch die Gemeinsamkeiten zwischen Varianten entstehen. Existierende SPL-Testansätze adressieren diese Herausforderungen z.B. durch die Reduktion der Anzahl an zu testenden Varianten. Jedoch wird weiterhin jede Variante unabhängig getestet, ohne dabei das Wissen über Gemeinsamkeiten und Variabilität auszunutzen, um den Testaufwand zu reduzieren. Des Weiteren muss sich die SPL-Entwicklung mit der Evolution von Software auseinandersetzen. Dies birgt weitere Herausforderungen für das SPL-Testen, da nicht nur für Varianten sondern auch für ihre Versionen die Qualität sichergestellt werden muss. In dieser Arbeit stellen wir ein Framework für das modellbasierte Regressionstesten von evolvierenden SPL vor, das die Herausforderungen des redundanten Testens und der Software-Evolution adressiert. Das Framework vereint Testmodellierung, Änderungsauswirkungsanalyse und automatische Testfallselektion, um einen inkrementellen Testprozess zu definieren, der Varianten und Variantenversionen unter Ausnutzung des Wissens über gemeinsame Funktionalität und dem Wiederverwendungspotential von Testartefakten und -resultaten effizient testet. Für die Testmodellierung entwickeln wir einen Ansatz, der Variabilitäts- sowie Versionsinformation von evolvierenden SPL gleichermaßen für die Modellierung einbezieht. Für die Änderungsauswirkungsanalyse definieren wir zwei Techniken, um zum einen Änderungen in Ausführungsabhängigkeiten zwischen zu testenden Varianten und ihren Versionen zu identifizieren und zum anderen die Auswirkungen eines Evolutionsschrittes auf die Variantenmenge zu bestimmen und zu klassifizieren. Für die Testfallselektion schlagen wir ein Abdeckungskriterium vor, das die Resultate der Auswirkungsanalyse einbezieht, um automatisierte Entscheidungen über einen Wiederholungstest von wiederverwendbaren Testfällen durchzuführen. Die abdeckungsgetriebene Testfallselektion ermöglicht somit die Reduktion der redundanten Testfallausführungen während des inkrementellen Testens von Varianten und Variantenversionen. Das Framework ist prototypisch implementiert und anhand von drei evolvierenden SPL evaluiert. Die Resultate zeigen, dass eine Aufwandsreduktion für das Testen evolvierender SPL erreicht wird

A Survey and Comparison of Industrial and Academic Research on the Evolution of Software Product Lines

Past research on software product lines has focused on the initial development of reusable assets and related challenges, such as cost estimation and implementation issues. Naturally, as software product lines are increasingly adopted throughout industry, their ongoing maintenance and evolution are getting more attention as well. However, it is not clear to what degree research is following this trend, and where the interests and demands of the industry lie. In this technical report, we provide a survey and comparison of selected publications on software product line maintenance and evolution at SPLC. In particular, we analyze and discuss similarities and differences of these papers with regard to their affiliation with industry and academia. From this, we infer directions for future research that pave the way for systematic and organized evolution of software product lines, from which industry may benefit as well.Comment: 8 page

SGSC Framework: Smart Government in Supply Chain Based on FODA

Smart System has implemented in government sector. There are varies Implementation that was utilized by research activities for numerous domains is very broad. Besides that, the Industry, transportation and health, also where such a system is incredibly beneficial. This study discuss supply chain and governmental link issue, coordination of all stakeholder in supply chain has to reflect the government role. It support with the condition in Indonesian government environment is unique. It is a challenge to construct smart system based on Feature Oriented Domain Analysis (FODA) approach. It can produce software product line (SPL). We proposed framework for develop software product line for smart supply chain in government sector. It is used to enhance and improve the development of software systems by multiple software system developers. It will be a guidance for construct smart government, and more specificity in supply chain for government system area environment. It is called SGSC Framework. It consists of four layers, such as optimization layer, integration layer, supply chain layer and data layer

Applying Software Product Lines to Build Autonomic Pervasive Systems

In this Master Thesis, we have proposed a model-driven Software Product Line (SPL) for developing autonomic pervasive systems. The work focusses on reusing the Variability knowledge from the SPL design to the SPL products. This Variability knowledge enables SPL products to deal with adaptation scenarios (evolution and involution) in an autonomic way.Cetina Englada, C. (2008). Applying Software Product Lines to Build Autonomic Pervasive Systems. http://hdl.handle.net/10251/12447Archivo delegad

Run-time Support to Manage Architectural Variability Speci ed with CVL

The execution context in which pervasive systems or mobile computing run changes continuously. Hence, applications for these systems should be adapted at run-time according to the current context. In order to implement a context-aware dynamic reconfiguration service, most approaches usually require to model at design-time both the list of all possible configurations and the plans to switch among them. In this paper we present an alternative approach for the automatic run-time generation of application configurations and the reconfiguration plans. The generated configurations are optimal regarding di erent criteria, such as functionality or resource consumption (e.g. battery or memory). This is achieved by: (1) modelling architectural variability at design-time using Common Variability Language (CVL), and (2) using a genetic algorithm that finds at run-time nearly-optimal configurations using the information provided by the variability model. We also specify a case study and we use it to evaluate our approach, showing that it is efficient and suitable for devices with scarce resources.Campus de Excelencia Internacional Andalucia Tech y proyectos de investigación TIN2008-01942, P09-TIC-5231 and INTER-TRUST FP7-317731

A systematic literature review on the semi-automatic configuration of extended product lines

Product line engineering has become essential in mass customisation given its ability to reduce production costs and time to market, and to improve product quality and customer satisfaction. In product line literature, mass customisation is known as product configuration. Currently, there are multiple heterogeneous contributions in the product line configuration domain. However, a secondary study that shows an overview of the progress, trends, and gaps faced by researchers in this domain is still missing. In this context, we provide a comprehensive systematic literature review to discover which approaches exist to support the configuration process of extended product lines and how these approaches perform in practice. Extend product lines consider non-functional properties in the product line modelling. We compare and classify a total of 66 primary studies from 2000 to 2016. Mainly, we give an in-depth view of techniques used by each work, how these techniques are evaluated and their main shortcomings. As main results, our review identified (i) the need to improve the quality of the evaluation of existing approaches, (ii) a lack of hybrid solutions to support multiple configuration constraints, and (iii) a need to improve scalability and performance conditions

Integration of Quality Attributes in Software Product Line Development

Different approaches for building modern software systems in complex and open environments have been proposed in the last few years. Some efforts try to apply Software Product Line (SPL) approach to take advantage of the massive reuse for producing software systems that share a common set of features. In general quality assurance is a crucial activity for success in software industry, but it is even more important when talking about Software Product Lines since the intensive reuse of assets makes the quality attributes (a measurable physical or abstract property of an entity) of the assets to be transmitted to the whole SPL scope. However, despite the importance that quality has in software product line development, most of the methodologies being applied in Software Product Line Development focus only on managing the commonalities and variability within the product line and not giving support to the non--¿ functional requirements that the products must fit. The main goal of this master final work is to introduce quality attributes in early stages of software product line development processes by means of the definition of a production plan that, on one hand, integrates quality as an additional view for describing the extension of the software product line and, on the other hand introduces the quality attributes as a decision factor during product configuration and when selecting among design alternatives. Our approach has been defined following the Model--¿ Driven Software Development paradigm. Therefore all the software artifacts defined had its correspondent metamodels and the processes defined rely on automated model transformations. Finally in order to illustrate the feasibility of the approach we have integrated the quality view in an SPL example in the context of safety critical embedded systems on the automotive domain.González Huerta, J. (2011). Integration of Quality Attributes in Software Product Line Development. http://hdl.handle.net/10251/15835Archivo delegad

Optimizing selection of competing features via feedback-directed evolutionary algorithms

Software that support various groups of customers usually require complicated configurations to attain different functionalities. To model the configuration options, feature model is proposed to capture the commonalities and competing variabilities of the product variants in software family or Software Product Line (SPL). A key challenge for deriving a new product is to find a set of features that do not have inconsistencies or conflicts, yet optimize multiple objectives (e.g., minimizing cost and maximizing number of features), which are often competing with each other. Existing works have attempted to make use of evolutionary algorithms (EAs) to address this problem. In this work, we incorporated a novel feedback-directed mechanism into existing EAs. Our empirical results have shown that our method has improved noticeably over all unguided version of EAs on the optimal feature selection. In particular, for case studies in SPLOT and LVAT repositories, the feedback-directed Indicator-Based EA (IBEA) has increased the number of correct solutions found by 72.33% and 75%, compared to unguided IBEA. In addition, by leveraging a pre-computed solution, we have found 34 sound solutions for Linux X86, which contains 6888 features, in less than 40 seconds.No Full Tex