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Search Results: 1 - 10 of 7304 matches for " Trendowicz Adam "
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Integrating Human Judgment and Data Analysis to Identify Factors Influencing Software Development Productivity
Trendowicz Adam,Ochs Michael,Wickenkamp Axel,Munch Jurgen
e-Informatica Software Engineering Journal , 2008,
Supporting Process Maturation with the Enhanced CoBRA Method
Adam Trendowicz,Jens Heidrich,Jürgen Münch
Computer Science , 2014,
Abstract: Cost estimation is a very crucial field for software developing companies. In the context of learning organizations, estimation applicability and accuracy are not the only acceptance criteria. The contribution of an estimation technique to the understanding and maturing of related organizational processes (such as identification of cost and productivity factors, measurement, data validation, model validation, model maintenance) has recently been gaining increasing importance. Yet, most of the proposed cost modeling approaches provide software engineers with hardly any assistance in supporting related processes. Insufficient support is provided for validating created cost models (including underlying data collection processes) or, if valid models are obtained, for applying them to achieve an organization's objectives such as improved productivity or reduced schedule. This paper presents an enhancement of the CoBRA(R) cost modeling method by systematically including additional quantitative methods into iterative analysis-feedback cycles. Applied at Oki Electric Industry Co., Ltd., Japan, the CoBRA(R) method contributed to the achievement of the following objectives, including: (1) maturation of existing measurement processes, (2) increased expertise of Oki software project decision makers regarding cost-related software processes, and, finally, (3) reduction of initial estimation error from an initial 120% down to 14%.
State of the Practice in Software Effort Estimation: A Survey and Literature Review
Adam Trendowicz,Jürgen Münch,Ross Jeffery
Computer Science , 2014, DOI: 10.1007/978-3-642-22386-0_18
Abstract: Effort estimation is a key factor for software project success, defined as delivering software of agreed quality and functionality within schedule and budget. Traditionally, effort estimation has been used for planning and tracking project resources. Effort estimation methods founded on those goals typically focus on providing exact estimates and usually do not support objectives that have recently become important within the software industry, such as systematic and reliable analysis of causal effort dependencies. This article presents the results of a study of software effort estimation from an industrial perspective. The study surveys industrial objectives, the abilities of software organizations to apply certain estimation methods, and actually applied practices of software effort estimation. Finally, requirements for effort estimation methods identified in the survey are compared against existing estimation methods.
A Deployment Process for Strategic Measurement Systems
Martin Kowalczyk,Henning Barthel,Jürgen Münch,Jens Heidrich,Adam Trendowicz
Computer Science , 2013,
Abstract: Explicitly linking software-related activities to an organisation's higher-level goals has been shown to be critical for organizational success. GQM+Strategies provides mechanisms for explicitly linking goals and strategies, based on goal-oriented strategic measurement systems. Deploying such strategic measurement systems in an organization is highly challenging. Experience has shown that a clear deployment strategy is needed for achieving sustainable success. In particular, an adequate deployment process as well as corresponding tool support can facilitate the deployment. This paper introduces the systematical GQM+Strategies deployment process and gives an overview of GQM+Strategies modelling and associated tool support. Additionally, it provides an overview of industrial applications and describes success factors and benefits for the usage of GQM+Strategies.
Comprehensive Landscapes for Software-related Quality Models
Michael Kl?s,Jens Heidrich,Jürgen Münch,Adam Trendowicz
Computer Science , 2014,
Abstract: Managing quality (such as service availability or process adherence) during the development, operation, and maintenance of software(-intensive) systems and services is a challenging task. Although many organizations need to define, control, measure, and improve various quality aspects of their devel- opment artifacts and processes, nearly no guidance is available on how to select, adapt, define, combine, use, and evolve quality models. Catalogs of quality models as well as selection and tailoring processes are widely missing. One essential reason for this tremendous lack of support is that software development is a highly context-dependent process. Therefore, quality models always need to be adaptable to the respective project goals and contexts. A first step towards better support for selecting and adapting quality models can be seen in a classification of existing quality models, especially with respect to their suitability for different purposes and contexts. Such a classification of quality models can be applied to provide an integrated overview of the variety of quality models. This article presents the idea of so called comprehensive quality model landscapes (CQMLs), which provide a classification scheme for quality models and help to get an overview of existing quality models and their relationships. The article describes the usage goals for such landscapes, presents a classification scheme, presents the initial concept of such landscapes, illustrates the concept with selected examples, and sketches open questions and future work.
Lessons Learned and Results from Applying Data-Driven Cost Estimation to Industrial Data Sets
Jens Heidrich,Adam Trendowicz,Jürgen Münch,Yasushi Ishigai,Kenji Yokoyama,Nahomi Kikuchi,T. Kawaguchi
Computer Science , 2014, DOI: 10.1109/QUATIC.2007.16
Abstract: The increasing availability of cost-relevant data in industry allows companies to apply data-intensive estimation methods. However, available data are often inconsistent, invalid, or incomplete, so that most of the existing data-intensive estimation methods cannot be applied. Only few estimation methods can deal with imperfect data to a certain extent (e.g., Optimized Set Reduction, OSR(c)). Results from evaluating these methods in practical environments are rare. This article describes a case study on the application of OSR(c) at Toshiba Information Systems (Japan) Corporation. An important result of the case study is that estimation accuracy significantly varies with the data sets used and the way of preprocessing these data. The study supports current results in the area of quantitative cost estimation and clearly illustrates typical problems. Experiences, lessons learned, and recommendations with respect to data preprocessing and data-intensive cost estimation in general are presented.
An Integrated Approach for Identifying Relevant Factors Influencing Software Development Productivity
Adam Trendowicz,Michael Ochs,Axel Wickenkamp,Jürgen Münch,Yasushi Ishigai,Takashi Kawaguchi
Computer Science , 2014, DOI: 10.1007/978-3-540-85279-7_18
Abstract: Managing software development productivity and effort are key issues in software organizations. Identifying the most relevant factors influencing project performance is essential for implementing business strategies by selecting and adjusting proper improvement activities. There is, however, a large number of potential influencing factors. This paper proposes a novel approach for identifying the most relevant factors influencing software development productivity. The method elicits relevant factors by integrating data analysis and expert judgment approaches by means of a multi-criteria decision support technique. Empirical evaluation of the method in an industrial context has indicated that it delivers a different set of factors compared to individual data- and expert-based factor selection methods. Moreover, application of the integrated method significantly improves the performance of effort estimation in terms of accuracy and precision. Finally, the study did not replicate the observation of similar investigations regarding improved estimation performance on the factor sets reduced by a data-based selection method.
Model-based Product Quality Evaluation with Multi-Criteria Decision Analysis
Adam Trendowicz,Michael Kl?s,Constanza Lampasona,Jürgen Münch,Christian K?rner,Matthias Saft
Computer Science , 2014,
Abstract: The ability to develop or evolve software or software-based systems/services with defined and guaranteed quality in a predictable way is becoming increasingly important. Essential - though not exclusive - prerequisites for this are the ability to model the relevant quality properties appropriately and the capability to perform reliable quality evaluations. Existing approaches for integrated quality modeling and evaluation are typically either narrowly focused or too generic and have proprietary ways for modeling and evaluating quality. This article sketches an ap- proach for modeling and evaluating quality properties in a uniform way, without losing the ability to build sufficiently detailed customized models for specific quality properties. The focus of this article is on the description of a multi-criteria aggregation mechanism that can be used for the evaluation. In addition, the underlying quality meta-model, an example application scenario, related work, initial application results, and an outlook on future research are presented.
Linking Software Development and Business Strategy Through Measurement
Victor R. Basili,Jens Heidrich,Mikael Lindvall,Jürgen Münch,Myrna Regardie,Dieter Rombach,Carolyn Seaman,Adam Trendowicz
Computer Science , 2013, DOI: 10.1109/MC.2010.108
Abstract: Most of today's products and services are software-based. Organizations that develop software want to maintain and improve their competitiveness by controlling software-related risks. To do this, they need to align their business goals with software development strategies and translate them into quantitative project management. There is also an increasing need to justify cost and resources for software and system development and other IT services by demonstrating their impact on an organisation's higher-level goals. For both, linking business goals and software-related efforts in an organization is necessary. However, this is a challenging task, and there is a lack of methods addressing this gap. The GQM+Strategies approach effectively links goals and strategies on all levels of an organization by means of goal-oriented measurement. The approach is based on rationales for deciding about options when operationalizing goals and for evaluating the success of strategies with respect to goals.
GQM+Strategies: A Comprehensive Methodology for Aligning Business Strategies with Software Measurement
Victor Basili,Jens Heidrich,Mikael Lindvall,Jürgen Münch,Myrna Regardie,Dieter Rombach,Carolyn Seaman,Adam Trendowicz
Computer Science , 2014,
Abstract: In software-intensive organizations, an organizational management system will not guarantee organizational success unless the business strategy can be translated into a set of operational software goals. The Goal Question Metric (GQM) approach has proven itself useful in a variety of industrial settings to support quantitative software project management. However, it does not address linking software measurement goals to higher-level goals of the organization in which the software is being developed. This linkage is important, as it helps to justify software measurement efforts and allows measurement data to contribute to higher-level decisions. In this paper, we propose a GQM+Strategies(R) measurement approach that builds on the GQM approach to plan and implement software measurement. GQM+Strategies(R) provides mechanisms for explicitly linking software measurement goals to higher-level goals for the software organization, and further to goals and strategies at the level of the entire business. The proposed method is illustrated in the context of an example application of the method.
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