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- Title
- A LIFE CYCLE SOFTWARE QUALITY MODEL USING BAYESIAN BELIEF NETWORKS.
- Creator
-
Beaver, Justin, Schiavone, Guy, University of Central Florida
- Abstract / Description
-
Software practitioners lack a consistent approach to assessing and predicting quality within their products. This research proposes a software quality model that accounts for the influences of development team skill/experience, process maturity, and problem complexity throughout the software engineering life cycle. The model is structured using Bayesian Belief Networks and, unlike previous efforts, uses widely-accepted software engineering standards and in-use industry techniques to quantify...
Show moreSoftware practitioners lack a consistent approach to assessing and predicting quality within their products. This research proposes a software quality model that accounts for the influences of development team skill/experience, process maturity, and problem complexity throughout the software engineering life cycle. The model is structured using Bayesian Belief Networks and, unlike previous efforts, uses widely-accepted software engineering standards and in-use industry techniques to quantify the indicators and measures of software quality. Data from 28 software engineering projects was acquired for this study, and was used for validation and comparison of the presented software quality models. Three Bayesian model structures are explored and the structure with the highest performance in terms of accuracy of fit and predictive validity is reported. In addition, the Bayesian Belief Networks are compared to both Least Squares Regression and Neural Networks in order to identify the technique is best suited to modeling software product quality. The results indicate that Bayesian Belief Networks outperform both Least Squares Regression and Neural Networks in terms of producing modeled software quality variables that fit the distribution of actual software quality values, and in accurately forecasting 25 different indicators of software quality. Between the Bayesian model structures, the simplest structure, which relates software quality variables to their correlated causal factors, was found to be the most effective in modeling software quality. In addition, the results reveal that the collective skill and experience of the development team, over process maturity or problem complexity, has the most significant impact on the quality of software products.
Show less - Date Issued
- 2006
- Identifier
- CFE0001367, ucf:46993
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0001367
- Title
- An adaptive integration architecture for software reuse.
- Creator
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Williams, Denver Robert Edward, Orooji, Ali, Engineering and Computer Science
- Abstract / Description
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University of Central Florida College of Engineering Thesis; The problem of building large, reliable software systems in a controlled, cost effective way, the so-called software crisis problem, is one of computer science's great challenges. From the very outset of computing as science, software reuse has been touted as a means to overcome the software crisis issue.
- Date Issued
- 2001
- Identifier
- CFR0000786, ucf:52928
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFR0000786
- Title
- INCREMENTAL LIFECYCLE VALIDATION OF KNOWLEDGE-BASED SYSTEMS THROUGH COMMONKADS.
- Creator
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Batarseh, Feras, Gonzalez, Avelino, University of Central Florida
- Abstract / Description
-
This dissertation introduces a novel validation method for knowledge-based systems (KBS).Validation is an essential phase in the development lifecycle of knowledge-based systems. Validation ensures that the system is valid, reliable and that it reflects the knowledge of the expert and meets the specifications. Although many validation methods have been introduced for knowledge-based systems, there is still a need for an incremental validation method based on a lifecycle model. Lifecycle...
Show moreThis dissertation introduces a novel validation method for knowledge-based systems (KBS).Validation is an essential phase in the development lifecycle of knowledge-based systems. Validation ensures that the system is valid, reliable and that it reflects the knowledge of the expert and meets the specifications. Although many validation methods have been introduced for knowledge-based systems, there is still a need for an incremental validation method based on a lifecycle model. Lifecycle models provide a general framework for the developer and a mapping technique from the system into the validation process. They support reusability, modularity and offer guidelines for knowledge engineers to achieve high quality systems. CommonKADS is a set of models that helps to represent and analyze knowledge-based systems. It offers a de facto standard for building knowledge-based systems. Additionally, CommonKADS is a knowledge representation-independent model. It has powerful models that can represent many domains. Defining an incremental validation method based on a conceptual lifecycle model (such as CommonKADS) has a number of advantages such as reducing time and effort, ease of implementation when having a template to follow, well-structured design, and better tracking of errors when they occur. Moreover, the validation method introduced in this dissertation is based on case testing and selecting an appropriate set of test cases to validate the system. The validation method defined makes use of results of prior test cases in an incremental validation procedure. This facilitates defining a minimal set of test cases that provides complete and effective system coverage. CommonKADS doesn't define validation, verification or testing in any of its models. This research seeks to establish a direct relation between validation and lifecycle models, and introduces a validation method for KBS embedded into CommonKADS.
Show less - Date Issued
- 2011
- Identifier
- CFE0003621, ucf:48879
- Format
- Document (PDF)
- PURL
- http://purl.flvc.org/ucf/fd/CFE0003621