958 resultados para Legacy Software
Resumo:
The evolvability of a software artifact is its capacity for producing heritable or reusable variants; the inverse quality is the artifact's inertia or resistance to evolutionary change. Evolvability in software systems may arise from engineering and/or self-organising processes. We describe our 'Conditional Growth' simulation model of software evolution and show how, it can be used to investigate evolvability from a self-organisation perspective. The model is derived from the Bak-Sneppen family of 'self-organised criticality' simulations. It shows good qualitative agreement with Lehman's 'laws of software evolution' and reproduces phenomena that have been observed empirically. The model suggests interesting predictions about the dynamics of evolvability and implies that much of the observed variability in software evolution can be accounted for by comparatively simple self-organising processes.
Resumo:
Nowadays the use of information and communication technology is becoming prevalent in many aspects of healthcare services from patient registration, to consultation, treatment and pathology tests request. Manual interface techniques have dominated data-capture activities in primary care and secondary care settings for decades. Despites the improvements made in IT, usability issues still remain over the use of I/O devices like the computer keyboard, touch-sensitive screens, light pen and barcodes. Furthermore, clinicians have to use several computer applications when providing healthcare services to patients. One of the problems faced by medical professionals is the lack of data integrity between the different software applications which in turn can hinder the provision of healthcare services tailored to the needs of the patients. The use of digital pen and paper technology integrated with legacy medical systems hold the promise of improving healthcare quality. This paper discusses the issue of data integrity in e-health systems and proposes the modelling of "Smart Forms" via semiotics to potentially improve integrity between legacy systems, making the work of medical professionals easier and improve the quality of care in primary care practices and hospitals.
Resumo:
This paper addresses the need for accurate predictions on the fault inflow, i.e. the number of faults found in the consecutive project weeks, in highly iterative processes. In such processes, in contrast to waterfall-like processes, fault repair and development of new features run almost in parallel. Given accurate predictions on fault inflow, managers could dynamically re-allocate resources between these different tasks in a more adequate way. Furthermore, managers could react with process improvements when the expected fault inflow is higher than desired. This study suggests software reliability growth models (SRGMs) for predicting fault inflow. Originally developed for traditional processes, the performance of these models in highly iterative processes is investigated. Additionally, a simple linear model is developed and compared to the SRGMs. The paper provides results from applying these models on fault data from three different industrial projects. One of the key findings of this study is that some SRGMs are applicable for predicting fault inflow in highly iterative processes. Moreover, the results show that the simple linear model represents a valid alternative to the SRGMs, as it provides reasonably accurate predictions and performs better in many cases.
Resumo:
This paper describes some of the preliminary outcomes of a UK project looking at control education. The focus is on two aspects: (i) the most important control concepts and theories for students doing just one or two courses and (ii) the effective use of software to improve student learning and engagement. There is also some discussion of the correct balance between teaching theory and practise. The paper gives examples from numerous UK universities and some industrial comment.
Using simulation to determine the sensibility of error sources for software effort estimation models