31,671 research outputs found
Systematic evaluation of software product line architectures
The architecture of a software product line is one of its most important artifacts as it represents an abstraction of the products that can be generated. It is crucial to evaluate the quality attributes of a product line architecture in order to: increase the productivity of the product line process and the quality of the products; provide a means to understand the potential behavior of the products and, consequently, decrease their time to market; and, improve the handling of the product line variability. The evaluation of product line architecture can serve as a basis to analyze the managerial and economical values of a product line for software managers and architects. Most of the current research on the evaluation of product line architecture does not take into account metrics directly obtained from UML models and their variabilities; the metrics used instead are difficult to be applied in general and to be used for quantitative analysis. This paper presents a Systematic Evaluation Method for UML-based Software Product Line Architecture, the SystEM-PLA. SystEM-PLA differs from current research as it provides stakeholders with a means to: (i) estimate and analyze potential products; (ii) use predefined basic UML-based metrics to compose quality attribute metrics; (iii) perform feasibility and trade-off analysis of a product line architecture with respect to its quality attributes; and, (iv) make the evaluation of product line architecture more flexible. An example using the SEI’s Arcade Game Maker (AGM) product line is presented as a proof of concept, illustrating SystEM-PLA activities. Metrics for complexity and extensibility quality attributes are defined and used to
perform a trade-off analysis
Towards guidelines for building a business case and gathering evidence of software reference architectures in industry
Background: Software reference architectures are becoming widely adopted by organizations that need to support the design and maintenance of software applications of a shared domain. For organizations that plan to adopt this architecture-centric approach, it becomes fundamental to know the return on investment and to understand how software reference architectures are designed, maintained, and used. Unfortunately, there is little evidence-based support to help organizations with these challenges.
Methods: We have conducted action research in an industry-academia collaboration between the GESSI research group and everis, a multinational IT consulting firm based in Spain.
Results: The results from such collaboration are being packaged in order to create guidelines that could be used in similar contexts as the one of everis. The main result of this paper is the construction of empirically-grounded guidelines that support organizations to decide on the adoption of software reference architectures and to gather evidence to improve RA-related practices.
Conclusions: The created guidelines could be used by other organizations outside of our industry-academia collaboration. With this goal in mind, we describe the guidelines in detail for their use.Peer ReviewedPostprint (published version
Characterizing and Subsetting Big Data Workloads
Big data benchmark suites must include a diversity of data and workloads to
be useful in fairly evaluating big data systems and architectures. However,
using truly comprehensive benchmarks poses great challenges for the
architecture community. First, we need to thoroughly understand the behaviors
of a variety of workloads. Second, our usual simulation-based research methods
become prohibitively expensive for big data. As big data is an emerging field,
more and more software stacks are being proposed to facilitate the development
of big data applications, which aggravates hese challenges. In this paper, we
first use Principle Component Analysis (PCA) to identify the most important
characteristics from 45 metrics to characterize big data workloads from
BigDataBench, a comprehensive big data benchmark suite. Second, we apply a
clustering technique to the principle components obtained from the PCA to
investigate the similarity among big data workloads, and we verify the
importance of including different software stacks for big data benchmarking.
Third, we select seven representative big data workloads by removing redundant
ones and release the BigDataBench simulation version, which is publicly
available from http://prof.ict.ac.cn/BigDataBench/simulatorversion/.Comment: 11 pages, 6 figures, 2014 IEEE International Symposium on Workload
Characterizatio
Improving cross-functional communication about product architecture
Product architecture decisions, such as product modularity, component commonality, and design reuse, are important for balancing costs, responsiveness, quality, and other important business objectives. Firms are challenged with complex tradeoffs between competing design priorities, face the need to facilitate communication between functional silos, and to learn from past experiences. In this paper we present a qualitative approach for systematically evaluating the product architecture of an existing product or product family, linking the original architecture objectives and actual experiences. The intended contribution of our research is to present a framework that brings together a diverse set of product architecture-related decisions that are relevant from a business point of view (and not from a technical point of view) and a set of business performance elements. This framework can be used in workshop that improves cross-functional communication about the product architecture of an existing product family, and this results in practical improvement actions for future architecture design projects. Initial experiences with this approach have been obtained in pilots with Philips domestic appliances & personal care, and Philips consumer electronics
Product line architecture recovery with outlier filtering in software families: the Apo-Games case study
Software product line (SPL) approach has been widely adopted to achieve systematic reuse in families of software products. Despite its benefits, developing an SPL from scratch requires high up-front investment. Because of that, organizations commonly create product variants with opportunistic reuse approaches (e.g., copy-and-paste or clone-and-own). However, maintenance and evolution of a large number of product variants is a challenging task. In this context, a family of products developed opportunistically is a good starting point to adopt SPLs, known as extractive approach for SPL adoption. One of the initial phases of the extractive approach is the recovery and definition of a product line architecture (PLA) based on existing software variants, to support variant derivation and also to allow the customization according to customers’ needs. The problem of defining a PLA from existing system variants is that some variants can become highly unrelated to their predecessors, known as outlier variants. The inclusion of outlier variants in the PLA recovery leads to additional effort and noise in the common structure and complicates architectural decisions. In this work, we present an automatic approach to identify and filter outlier variants during the recovery and definition of PLAs. Our approach identifies the minimum subset of cross-product architectural information for an effective PLA recovery. To evaluate our approach, we focus on real-world variants of the Apo-Games family. We recover a PLA taking as input 34 Apo-Game variants developed by using opportunistic reuse. The results provided evidence that our automatic approach is able to identify and filter outlier variants, allowing to eliminate exclusive packages and classes without removing the whole variant. We consider that the recovered PLA can help domain experts to take informed decisions to support SPL adoption.This research was partially funded by INES 2.0; CNPq grants 465614/2014-0 and 408356/2018-9; and FAPESB grants JCB0060/2016 and BOL2443/201
Report from GI-Dagstuhl Seminar 16394: Software Performance Engineering in the DevOps World
This report documents the program and the outcomes of GI-Dagstuhl Seminar
16394 "Software Performance Engineering in the DevOps World".
The seminar addressed the problem of performance-aware DevOps. Both, DevOps
and performance engineering have been growing trends over the past one to two
years, in no small part due to the rise in importance of identifying
performance anomalies in the operations (Ops) of cloud and big data systems and
feeding these back to the development (Dev). However, so far, the research
community has treated software engineering, performance engineering, and cloud
computing mostly as individual research areas. We aimed to identify
cross-community collaboration, and to set the path for long-lasting
collaborations towards performance-aware DevOps.
The main goal of the seminar was to bring together young researchers (PhD
students in a later stage of their PhD, as well as PostDocs or Junior
Professors) in the areas of (i) software engineering, (ii) performance
engineering, and (iii) cloud computing and big data to present their current
research projects, to exchange experience and expertise, to discuss research
challenges, and to develop ideas for future collaborations
Survey of the State of the Art in Natural Language Generation: Core tasks, applications and evaluation
This paper surveys the current state of the art in Natural Language
Generation (NLG), defined as the task of generating text or speech from
non-linguistic input. A survey of NLG is timely in view of the changes that the
field has undergone over the past decade or so, especially in relation to new
(usually data-driven) methods, as well as new applications of NLG technology.
This survey therefore aims to (a) give an up-to-date synthesis of research on
the core tasks in NLG and the architectures adopted in which such tasks are
organised; (b) highlight a number of relatively recent research topics that
have arisen partly as a result of growing synergies between NLG and other areas
of artificial intelligence; (c) draw attention to the challenges in NLG
evaluation, relating them to similar challenges faced in other areas of Natural
Language Processing, with an emphasis on different evaluation methods and the
relationships between them.Comment: Published in Journal of AI Research (JAIR), volume 61, pp 75-170. 118
pages, 8 figures, 1 tabl
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