Enabling reuse-based software development of large-scale systems

Selby, Richard W.

doi:10.1109/tse.2005.69

Cited by 126 publications

(108 citation statements)

References 56 publications

(80 reference statements)

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“…Phase 2 would cover more specific products but at the time of publication, Phase 2 was still under planning and the savings were so far only estimated in the paper. The final list therefore includes the following eleven papers ordered here after the year of publication: , (Thomas et al, 1997), , , (Morisio et al, 2002), , (Mohagheghi et al, 2004) 1 , (Baldassarre et al, 2005), , (Selby, 2005), and (Zhang and Jarzabek, 2005).…”

Section: Paper Inclusion Criteriamentioning

confidence: 99%

“…When a paper does not provide information on an attribute, the label "-" is used. The conclusions may be summarized as: − Study type: Thomas et al (1997) and Selby (2005) do not discuss the study type. For the others, we have shown the study type both from the authors' and the review's perspective.…”

Section: Objects Types Of Studies Scale Publication Channel and Yearmentioning

confidence: 99%

“…The term "case study" is often used in literature to cover all studies where some data on "cases" are presented. The review has identified four case studies (Thomas et al, 1997) (Mohagheghi et al, 2004) (Baldassarre et al, 2005) (Selby, 2005), three exploratory case studies ) 2 (Morisio et al, 2002), three experience reports (Zhang and Jarzabek, 2005), and one example application Succi et al (2001) and Tomer et al (2004) have not reported programming language, and four of the papers (Baldassarre et al, 2005) (Zhang and Jarzabek, 2005) have not reported the size of products or the reusable assets, where in (Zhang and Jarzabek, 2005) it is not clear whether 4.5 KLOC is the total size or the mean size of applications. There is variation in domain and programming languages.…”

Section: Objects Types Of Studies Scale Publication Channel and Yearmentioning

confidence: 99%

“…− Medium-scale studies (M): Three studies ) (Baldassarre et al, 2005) cover larger products than the first group but less or around 100 KLOC, and still the objects of study are few. − Large-scale studies (L): Three studies (Thomas et al, 1997) (Mohagheghi et al, 2004) (Selby, 2005) cover products with software size more than 100 KLOC or cover a large number of objects.…”

Section: Objects Types Of Studies Scale Publication Channel and Yearmentioning

confidence: 99%

“…-Compositional: ) and ; -Reuse of templates: (Baldassarre et al, 2005); -Reuse of software modules or components: (Thomas et al, 1997), , Morad and Kuflik (2005) and (Selby, 2005); -OO frameworks: (Morisio et al, 2002); -Domain engineering: (Mohagheghi et al, 2004) with a layered software architecture, and (Zhang and Jarzabek, 2005) with metacomponents; -Component-based reuse: (Mohagheghi et al, 2004); -Generative programming: none;…”

Section: Technical Approachmentioning

confidence: 99%

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Quality, productivity and economic benefits of software reuse: a review of industrial studies

2007

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Abstract. Systematic software reuse is proposed to increase productivity and software quality and lead to economic benefits. Reports of successful software reuse programs in industry have been published. However, there has been little effort to organize the evidence systematically and appraise it. This review aims to assess the effects of software reuse in industrial contexts. Journals and major conferences between 1994 and 2005 were searched to find observational studies and experiments conducted in industry, returning eleven papers of observational type. Systematic software reuse is significantly related to lower problem (defect, fault or error) density in five studies and to decreased effort spent on correcting problems in three studies. The review found evidence for significant gains in apparent productivity in three studies. Other significant benefits of software reuse were reported in single studies or the results were inconsistent. Evidence from industry is sparse and combining results was done by votecounting. Researchers should pay more attention to using comparable metrics, performing longitudinal studies, and explaining the results and impact on industry.

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Section: Paper Inclusion Criteriamentioning

confidence: 99%

Section: Objects Types Of Studies Scale Publication Channel and Yearmentioning

confidence: 99%

Section: Objects Types Of Studies Scale Publication Channel and Yearmentioning

confidence: 99%

Section: Objects Types Of Studies Scale Publication Channel and Yearmentioning

confidence: 99%

Section: Technical Approachmentioning

confidence: 99%

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Quality, productivity and economic benefits of software reuse: a review of industrial studies

2007

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Software Quality Classification Models

Shin

Goel

2008

Wiley Encyclopedia of Computer Science and Engineering

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This article addresses the development of analytical models to identify fault‐prone components. Because of the lack of physical underpinnings, the software development mechanism is not understood well enough to derive theory‐based models. Therefore, these models are obtained from experimental or historical data after an iterative process. Some commonly used techniques and a recent methodology based on radial basis functions are described. A case study is presented to illustrate the model's development, evaluation, and selection process.

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11.4.1 Generating Predictive Models Using Decision Trees and Neural Networks for Large‐Scale Systems Engineering

Selby

2006

INCOSE International Symp

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Systems engineering must tackle the challenges of computational systems that are increasingly large-scale and software-intensive in terms of system size, component breadth and maturity, and development heterogeneity. This research describes and empirically evaluates techniques for generating predictive models for enabling large-scale system development and management. We describe two types of metric-driven decision models, decision trees and neural networks, which classify software components in large systems according to their likelihood of having user-specified properties such as high fault-proneness or high development effort. The metric-driven decision models enable coarse-grain analysis of large-scale multi-component heterogeneous systems, and they identify high-payoff areas for directing the application of finegrain analysis techniques for fault detection or redesign. The decision models serve as metric integration mechanisms that enable the synergistic use of numerous metrics simultaneously and integrate measurements collected by development tools or infrastructure. Model generation techniques automatically generate the decision models to calibrate them to new projects and organizations.We evaluate the predictive effectiveness of the decision models in terms of correctness, consistency, and completeness using fault and effort data from large NASA systems. Correctness is defined as the percent of components correctly identified, consistency is defined as 100% minus the percent of false positives, and completeness is defined as 100% minus the percent of false negatives. On average, the decision models had 83.44% correctness, 71.96% consistency, and 65.25% completeness in predictions of high fault and high effort software components. The network models had 89.63% correctness, 79.49% consistency, and 69.09% completeness, while the tree models had 77.25% correctness, 64.42% consistency, and 61.40% completeness. Non-parametric ANOVA comparisons showed that the network models were statistically more accurate than the tree models (α < 0.0001).

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Enabling reuse-based software development of large-scale systems

Cited by 126 publications

References 56 publications

Quality, productivity and economic benefits of software reuse: a review of industrial studies

Quality, productivity and economic benefits of software reuse: a review of industrial studies

Software Quality Classification Models

11.4.1 Generating Predictive Models Using Decision Trees and Neural Networks for Large‐Scale Systems Engineering

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