33,973 research outputs found
Data-Driven Application Maintenance: Views from the Trenches
In this paper we present our experience during design, development, and pilot
deployments of a data-driven machine learning based application maintenance
solution. We implemented a proof of concept to address a spectrum of
interrelated problems encountered in application maintenance projects including
duplicate incident ticket identification, assignee recommendation, theme
mining, and mapping of incidents to business processes. In the context of IT
services, these problems are frequently encountered, yet there is a gap in
bringing automation and optimization. Despite long-standing research around
mining and analysis of software repositories, such research outputs are not
adopted well in practice due to the constraints these solutions impose on the
users. We discuss need for designing pragmatic solutions with low barriers to
adoption and addressing right level of complexity of problems with respect to
underlying business constraints and nature of data.Comment: Earlier version of paper appearing in proceedings of the 4th
International Workshop on Software Engineering Research and Industrial
Practice (SER&IP), IEEE Press, pp. 48-54, 201
A Framework for Evaluating Model-Driven Self-adaptive Software Systems
In the last few years, Model Driven Development (MDD), Component-based
Software Development (CBSD), and context-oriented software have become
interesting alternatives for the design and construction of self-adaptive
software systems. In general, the ultimate goal of these technologies is to be
able to reduce development costs and effort, while improving the modularity,
flexibility, adaptability, and reliability of software systems. An analysis of
these technologies shows them all to include the principle of the separation of
concerns, and their further integration is a key factor to obtaining
high-quality and self-adaptable software systems. Each technology identifies
different concerns and deals with them separately in order to specify the
design of the self-adaptive applications, and, at the same time, support
software with adaptability and context-awareness. This research studies the
development methodologies that employ the principles of model-driven
development in building self-adaptive software systems. To this aim, this
article proposes an evaluation framework for analysing and evaluating the
features of model-driven approaches and their ability to support software with
self-adaptability and dependability in highly dynamic contextual environment.
Such evaluation framework can facilitate the software developers on selecting a
development methodology that suits their software requirements and reduces the
development effort of building self-adaptive software systems. This study
highlights the major drawbacks of the propped model-driven approaches in the
related works, and emphasise on considering the volatile aspects of
self-adaptive software in the analysis, design and implementation phases of the
development methodologies. In addition, we argue that the development
methodologies should leave the selection of modelling languages and modelling
tools to the software developers.Comment: model-driven architecture, COP, AOP, component composition,
self-adaptive application, context oriented software developmen
Automated software quality visualisation using fuzzy logic techniques
In the past decade there has been a concerted effort by the software industry to improve the quality of its products. This has led to the inception of various techniques with which to control and measure the process involved in software development. Methods like the Capability Maturity Model have introduced processes and strategies that require measurement in the form of software metrics. With the ever increasing number of software metrics being introduced by capability based processes, software development organisations are finding it more difficult to understand and interpret metric scores. This is particularly problematic for senior management and project managers where analysis of the actual data is not feasible. This paper proposes a method with which to visually represent metric scores so that managers can easily see how their organisation is performing relative to quality goals set for each type of metric. Acting primarily as a proof of concept and prototype, we suggest ways in which real customer needs can be translated into a feasible technical solution. The solution itself visualises metric scores in the form of a tree structure and utilises Fuzzy Logic techniques, XGMML, Web Services and the .NET Framework. Future work is proposed to extend the system from the prototype stage and to overcome a problem with the masking of poor scores
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