A neural fuzzy system to evaluate software development productivity

Baisch, Ekkehard; Bleile, Thomas; Belschner, R.

doi:10.1109/icsmc.1995.538521

Cited by 9 publications

(1 citation statement)

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“…In IEEE Xplore, the search returned 9 results, but all of them were not relevant because they use "quantified product" as part of composite terms, such as quantified product requirement (Ebert and Riegg, 1991), quantified product metrics (Baisch et al, 1995) or quantified product feature matrix (John et al, 2006).…”

Section: Related Work and Literature Analysismentioning

confidence: 99%

Quantified Products: Case Studies, Features and their Design Implications

Sandkuhl

2023

Informatica

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In many industrial sectors, the current digitalization trend resulted in new products and services that exploit the potential of built-in sensors, actuators, and control systems. The business models related to these products and services usually are data-driven and integrated into digital ecosystems. Quantified products (QP) are a new product category that exploits data of individual product instances and fleets of instances. A quantified product is a product whose instances collect data about themselves that can be measured or, by design, leave traces of data. The QP design has to consider what dependencies exist between the actual product, services related to the product, and the digital ecosystem of the services. By investigating three industrial case studies, the paper contributes to a better understanding of typical features of QP and the implications of these features for the design of products and services. For this purpose, we combine the analysis of features of QP potentially affecting design with an analysis of dependencies between features. The main contributions of the work are (1) three case studies describing QP design and development, (2) a set of recurring features of QPs derived from the cases, and (3) a feature model capturing design dependencies of these features.

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Section: Related Work and Literature Analysismentioning

confidence: 99%

Quantified Products: Case Studies, Features and their Design Implications

Sandkuhl

2023

Informatica

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Computational intelligence and visual computing: an emerging technology for software engineering

Pedrycz

2002

Soft Computing

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The discipline of Software Engineering is abstract and complex with all its endeavors being cast in a knowledge-intensive environment. It is not surprising that there have been a number of important initiatives that have attempted to address a burning need for solid development tools and comprehensive environments supporting an in-depth analysis. The objective of this study is to discuss a role of Computational Intelligence (CI) and visual computing being viewed as a sound methodological and algorithmic environment for knowledge-oriented Software Engineering. The CI itself is regarded as a synergistic consortium of granular computing (including fuzzy sets) promoting abstraction, neurocomputing supporting various learning schemes and evolutionary computing providing important faculties of global optimization. By its very nature, CI embraces a diversity of design paradigms; in particular it promotes a top-down approach (when exploiting fuzzy sets first and afterwards working in the neural network environment) or bottom-up style (where these two technologies are used in a reverse order). Visual computing is inherently associated with CI: it is human-centric where fuzzy sets make visualization activities feasible. Fuzzy sets are treated as a graphic means of accepting information from users. They are regarded as a vehicle used to visualize results in a linguistic manner. Software Engineering and CI are highly compatible: they are knowledge-intensive, human-oriented, and have to deal with various manifestations of the abstract world of software constructs and thought processes. This multifaceted conceptual compatibility is a prerequisite for the development of vital synergistic links that bring the technology of CI into Software Engineering. The symbiosis accrues considerable benefits for both technologies by posing new categories of challenging and highly stimulating problems. The facet of visual computing is essential in handling of software processes and software products. The intent of this study is to provide a general overview of this new development in Software Engineering. In particular, we highlight a number of selected and most visible trends occurring at the junction of CI and Software Engineering. Furthermore we discuss several specific applications of the technology of CI to software cost estimation, analysis of software measures and neural models of software quality. Introductory remarksThe research agenda of Software Engineering is ambitious: we are after building and maintaining high quality software products and develop better software processes. The difficulties in accomplishing this main goal are substantial.In an attempt to pursue fruitful developments it is beneficial to identify sources of such difficulties. The bottom line is this: products and processes of Software Engineering are very human-intensive while at the same time they deal with high level abstract concepts and result in constructs whose functioning is not governed by the laws of physics. There is no direct use of ideas such as contin...

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