1,720,984 research outputs found
An approach for modeling and detecting software performance antipatterns based on first-order logics
No full textThe problem of interpreting the results of performance analysis is quite critical in the software performance domain. Mean values, variances and probability distributions are hard to interpret for providing feedback to software architects. Instead, what architects expect are solutions to performance problems, possibly in the form of architectural alternatives (e.g. split a software component in two components and re-deploy one of them). In a software performance engineering process, the path from analysis results to software design or implementation alternatives is still based on the skills and experience of analysts. In this paper, we propose an approach for the generation of feedback based on performance antipatterns. In particular, we focus on the representation and detection of antipatterns. To this goal, we model performance antipatterns as logical predicates and we build an engine, based on such predicates, aimed at detecting performance antipatterns in an XML representation of the software system. Finally, we show the approach at work on a case study
Experimenting the Influence of Numerical Thresholds on Model-based Detection and Refactoring of Performance Antipatterns
No full textPerformance antipattern detection through fUML model library
No full textIdentifying performance problems is critical in the software design, mostly because the results of performance analysis (i.e., mean values, variances, and probability distributions) are difficult to be interpreted for providing feedback to software designers. Performance antipatterns support the interpretation of performance analysis results and help to fill the gap between numbers and design alternatives. In this paper, we present a model-driven framework that enables an early detection of performance antipatterns, i.e., without generating performance models. Specific design features (e.g., the number of sent messages) are monitored while simulating the specified software model, in order to point out the model elements that most likely contribute for performance flaws. To this end, we propose to use fUML models instrumented with a reusable library that provides data structures (as Classes) and algorithms (as Activities) to detect performance antipatterns while simulating the fUML model itself. A case study is provided to show our framework at work, its current capabilities and future challenges
Energy-aware adaptive techniques for information diffusion in ungoverned peer-to-peer networks
No full textThe problem of handling emergency situations (e.g., earthquakes, tornados, hurricanes, political rebellions, etc.) is very challenging since the fixed network infrastructures can become unusable, and it is fundamental to build a peer-to-peer network intended to spread information among people involved in the emergency. The goal of this paper is to propose an approach that enables an adaptive behavior of ungoverned communication networks with the purpose of maximizing the information diffusion and minimizing the energy consumption of the communication devices (e.g., smartphones). We introduce an energy-aware gossip algorithm to adapt the message passing methodology among the involved devices on the basis of their battery level, while guaranteeing the information diffusion within a certain geographical area and minimizing the overall energy consumption. The approach is implemented in a simulation context that allows to quantify the percentage of area coverage within a certain interval of time by adapting the process of message passing on the basis of devices' battery level. Experimental results demonstrate that our approach outperforms classic broadcast algorithms up to 85.58% in terms of energy consumption
An Architectural Framework for Analyzing Tradeoffs between Software Security and Performance
No full text
SMILE, a Simple Middleware Indipendent LayEr for distributed mobile applications
No full textIn this paper we introduce SMILE (Simple Middleware Independent LayEr), a framework whose main purpose is to facilitate the development of distributed applications. In the SMILE abstraction an application is composed by a set of processes that exchange information. The interfaces of these processes are described using WSDL or by an equivalent UML definition. Using the open source AndroMDA tool and starting from the UML interface specification we are able to generate the skeleton of SMILE applications and most part of their business logic. An application developed using SMILE can run on different middleware platforms just changing its binding, i.e. the code that adapts SMILE to a given middleware. We have implemented bindings to CORBA, JAVARMI, JADE, JXTA and to an our own communication mechanism based on SIP suitable for mobile devices. At the end we hint at the usage of SMILE in service composition and present some prototype applications
Towards a Continuous Model-Based Engineering Process for QoS-Aware Self-adaptive Systems
Full text linkModern information systems connecting software, physical systems, and people, are usually characterized by high dynamism. These dynamics introduce uncertainties, which in turn may harm the quality of service and lead to incomplete, inaccurate, and unreliable results. In this context, self-adaptation is considered as an effective approach for managing run-time uncertainty. However, classical approaches for quality engineering are not suitable to deal with run-time adaptation, as they are mainly used to derive the steady-state solutions of a system at design-time. In this paper, we envision a Continuous Model-based Engineering Process that makes use of architectural analysis in conjunction with experimentation to have a wider understanding of the system under development. These two activities are performed incrementally, and jointly used in a feedback loop to provide insights about the quality of the system-to-be
Going Beyond Counting First Authors in Author Co-citation Analysis
Full text linkThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
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