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Proceedings ArticleDOI

A product-process dependency definition method

TLDR
In this paper, a method for developing product/process dependency models (PPDMs) for product driven software process improvement is described, which is based on sound knowledge about the dependencies between software product quality attributes and software development processes.
Abstract
The success of most software companies depends largely on software product quality. High product quality is usually a result of advanced software development processes. Hence, improvement actions should be selected based on sound knowledge about the dependencies between software product quality attributes and software development processes. The article describes a method for developing product/process dependency models (PPDMs) for product driven software process improvement. The basic idea of the PPDM approach is that there are dependencies between product quality attributes, which are examined according to ISO 9126, and the software processes, which are assessed with the BOOTSTRAP methodology for example. The Goal-Question-Metric approach is used for product/process dependency hypothesis generation, analysis, and validation. We claim that after finding and using these dependencies, it is possible to focus improvement activities precisely and use resources more efficiently. The approach is currently being applied in three industrial applications in the ESPRIT project PROFES.

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Citations
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Development and evaluation of software process improvement methods

TL;DR: This dissertation develops, presents and argues for the SPI methods embodying characteristics directing towards successful process improvement, and incrementally develops and evaluates SPI methods, incorporating means to achieve the above-mentioned critical success factors.

Product focused software process improvement : SPI in the embedded software domain

TL;DR: It is important to note that organisations cannot learn: the individual people can learn and learn together; this definition reflects that learning happens when new insights arise.
Journal ArticleDOI

Approaches to promote product quality within software process improvement initiatives

TL;DR: This paper aims to provide an overview of an up-to-date state-of-the-art regarding initiatives that focus on promoting product quality improvement by applying SPI approaches by conducting a systematic mapping study.
Proceedings ArticleDOI

Applications of measurement in product-focused process improvement: a comparative industrial case study

TL;DR: A comparative case study investigates three different ways of applying GQM in product-focused process improvement: long-term GqM measurement programmes at the application sites to better understand and improve software products and processes, and G QM-based construction and validation of product/process dependency models.
References
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Book

Classification and regression trees

Leo Breiman
TL;DR: The methodology used to construct tree structured rules is the focus of a monograph as mentioned in this paper, covering the use of trees as a data analysis method, and in a more mathematical framework, proving some of their fundamental properties.
Journal ArticleDOI

Rough sets

TL;DR: This approach seems to be of fundamental importance to artificial intelligence (AI) and cognitive sciences, especially in the areas of machine learning, knowledge acquisition, decision analysis, knowledge discovery from databases, expert systems, decision support systems, inductive reasoning, and pattern recognition.
Journal ArticleDOI

Practical Guidelines for Measurement-based Process Improvement

TL;DR: In this paper, the authors present guidelines for using measurement in the context of process improvement in software development, including setting measurement goals, defining explicit measurement models, and implementing data collection procedures.
Journal ArticleDOI

Developing interpretable models with optimized set reduction for identifying high-risk software components

TL;DR: The authors present the optimized set reduction approach for constructing multivariate stochastic models for predicting high-risk system components and results obtained by classifying Ada components into two classes are presented.