201,796 research outputs found
Why (and How) Networks Should Run Themselves
The proliferation of networked devices, systems, and applications that we
depend on every day makes managing networks more important than ever. The
increasing security, availability, and performance demands of these
applications suggest that these increasingly difficult network management
problems be solved in real time, across a complex web of interacting protocols
and systems. Alas, just as the importance of network management has increased,
the network has grown so complex that it is seemingly unmanageable. In this new
era, network management requires a fundamentally new approach. Instead of
optimizations based on closed-form analysis of individual protocols, network
operators need data-driven, machine-learning-based models of end-to-end and
application performance based on high-level policy goals and a holistic view of
the underlying components. Instead of anomaly detection algorithms that operate
on offline analysis of network traces, operators need classification and
detection algorithms that can make real-time, closed-loop decisions. Networks
should learn to drive themselves. This paper explores this concept, discussing
how we might attain this ambitious goal by more closely coupling measurement
with real-time control and by relying on learning for inference and prediction
about a networked application or system, as opposed to closed-form analysis of
individual protocols
Global Innovations in Measurement and Evaluation
We researched the latest developments in theory and practice in measurement and evaluation. And we found that new thinking, techniques, and technology are influencing and improving practice. This report highlights 8 developments that we think have the greatest potential to improve evaluation and programme design, and the careful collection and use of data. In it, we seek to inform and inspire—to celebrate what is possible, and encourage wider application of these ideas
Software reliability and dependability: a roadmap
Shifting the focus from software reliability to user-centred measures of dependability in complete software-based systems. Influencing design practice to facilitate dependability assessment. Propagating awareness of dependability issues and the use of existing, useful methods. Injecting some rigour in the use of process-related evidence for dependability assessment. Better understanding issues of diversity and variation as drivers of dependability. Bev Littlewood is founder-Director of the Centre for Software Reliability, and Professor of Software Engineering at City University, London. Prof Littlewood has worked for many years on problems associated with the modelling and evaluation of the dependability of software-based systems; he has published many papers in international journals and conference proceedings and has edited several books. Much of this work has been carried out in collaborative projects, including the successful EC-funded projects SHIP, PDCS, PDCS2, DeVa. He has been employed as a consultant t
Mobile Agents for Mobile Tourists: A User Evaluation of Gulliver's Genie
How mobile computing applications and services may be best designed, implemented and deployed remains the subject of much research. One alternative approach to developing software for mobile users that is receiving increasing attention from the research community is that of one based on intelligent agents. Recent advances in mobile computing technology have made such an approach feasible. We present an overview of the design and implementation of an archetypical mobile computing application, namely that of an electronic tourist guide. This guide is unique in that it comprises a suite of intelligent agents that conform to the strong intentional stance. However, the focus of this paper is primarily concerned with the results of detailed user evaluations conducted on this system. Within the literature, comprehensive evaluations of mobile context-sensitive systems are sparse and therefore, this paper seeks, in part, to address this deficiency
A Quality Model for Actionable Analytics in Rapid Software Development
Background: Accessing relevant data on the product, process, and usage
perspectives of software as well as integrating and analyzing such data is
crucial for getting reliable and timely actionable insights aimed at
continuously managing software quality in Rapid Software Development (RSD). In
this context, several software analytics tools have been developed in recent
years. However, there is a lack of explainable software analytics that software
practitioners trust. Aims: We aimed at creating a quality model (called
Q-Rapids quality model) for actionable analytics in RSD, implementing it, and
evaluating its understandability and relevance. Method: We performed workshops
at four companies in order to determine relevant metrics as well as product and
process factors. We also elicited how these metrics and factors are used and
interpreted by practitioners when making decisions in RSD. We specified the
Q-Rapids quality model by comparing and integrating the results of the four
workshops. Then we implemented the Q-Rapids tool to support the usage of the
Q-Rapids quality model as well as the gathering, integration, and analysis of
the required data. Afterwards we installed the Q-Rapids tool in the four
companies and performed semi-structured interviews with eight product owners to
evaluate the understandability and relevance of the Q-Rapids quality model.
Results: The participants of the evaluation perceived the metrics as well as
the product and process factors of the Q-Rapids quality model as
understandable. Also, they considered the Q-Rapids quality model relevant for
identifying product and process deficiencies (e.g., blocking code situations).
Conclusions: By means of heterogeneous data sources, the Q-Rapids quality model
enables detecting problems that take more time to find manually and adds
transparency among the perspectives of system, process, and usage.Comment: This is an Author's Accepted Manuscript of a paper to be published by
IEEE in the 44th Euromicro Conference on Software Engineering and Advanced
Applications (SEAA) 2018. The final authenticated version will be available
onlin
Estimating, planning and managing Agile Web development projects under a value-based perspective
Context: The processes of estimating, planning and managing are crucial for software development projects,
since the results must be related to several business strategies. The broad expansion of the Internet
and the global and interconnected economy make Web development projects be often characterized by
expressions like delivering as soon as possible, reducing time to market and adapting to undefined
requirements. In this kind of environment, traditional methodologies based on predictive techniques
sometimes do not offer very satisfactory results. The rise of Agile methodologies and practices has
provided some useful tools that, combined with Web Engineering techniques, can help to establish a
framework to estimate, manage and plan Web development projects.
Objective: This paper presents a proposal for estimating, planning and managing Web projects, by
combining some existing Agile techniques with Web Engineering principles, presenting them as an
unified framework which uses the business value to guide the delivery of features.
Method: The proposal is analyzed by means of a case study, including a real-life project, in order to obtain
relevant conclusions.
Results: The results achieved after using the framework in a development project are presented, including
interesting results on project planning and estimation, as well as on team productivity throughout the
project.
Conclusion: It is concluded that the framework can be useful in order to better manage Web-based
projects, through a continuous value-based estimation and management process.Ministerio de EconomĂa y Competitividad TIN2013-46928-C3-3-
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