data types Sorting and searching parallel and distributed algorithms 3. [AR] Computer Architecture

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Guide to the Software Engineering Body of Knowledge

This paper summarizes the current state of the art and recent trends in software engineering economics. It provides an overview of economic analysis techniques and their applicability to software engineering and management. It surveys the field of software cost estimation, including the major estimation techniques available, the state of the art in algorithmic cost models, and the outstanding research issues in software cost estimation.

Software engineering economics

No single software fault-detection technique is capable of addressing all fault-detection concerns. Similarly to software reviews and testing, static analysis tools (or automated static analysis) can be used to remove defects prior to release of a software product. To determine to what extent automated static analysis can help in the economic production of a high-quality product, we have analyzed static analysis faults and test and customer-reported failures for three large-scale industrial software systems developed at Nortel Networks. The data indicate that automated static analysis is an affordable means of software fault detection. Using the orthogonal defect classification scheme, we found that automated static analysis is effective at identifying assignment and checking faults, allowing the later software production phases to focus on more complex, functional, and algorithmic faults. A majority of the defects found by automated static analysis appear to be produced by a few key types of programmer errors and some of these types have the potential to cause security vulnerabilities. Statistical analysis results indicate the number of automated static analysis faults can be effective for identifying problem modules. Our results indicate static analysis tools are complementary to other fault-detection techniques for the economic production of a high-quality software product.

/pdf/on-the-value-of-static-analysis-for-fault-detection-in-3qwbv6ko1f.pdf

On the value of static analysis for fault detection in software

Despite significant progress in the last 15 years, implementing a successful measurement program for software development is still a challenging undertaking. Most problems are not of theoretical but of methodological or practical nature. In this article, we present lessons learned from experiences with goal-oriented measurement. We structure them into practical guidelines for efficient and useful software measurement aimed at process improvement in industry. Issues related to setting measurement goals, defining explicit measurement models, and implementing data collection procedures are addressed from a practical perspective. In addition, guidelines for using measurement in the context of process improvement are provided.

Practical Guidelines for Measurement-based Process Improvement

ion sheets are a powerful tool that can be used during the set-up of the measurement programme: information from interviews can be organised in a structured way and copied from the abstraction sheets into the GQM plan. However, abstraction sheets can also be used for structuring the presentation and interpretation of measurement data during feedback sessions. In fact, an abstraction sheet is a one-page summary of a GQM plan. Not all direct measurements defined in a GQM plan are represented on abstraction sheets, only the basic ones that reflect the most important metrics. An example of an abstraction sheet is shown in Figure 6-4. Deliverable(s) 6.3: Set of interview reports and abstraction sheets. Object Purpose Quality Focus Viewpoint Delivered Understanding Reliability and Project Team Product its causes Quality Focus Number of failures: • by severity • by detection group • number of faults • by module Variation Factors

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The Goal/Question/Metric Method:

Schlumberger RPS (Retail Petroleum Systems) integrated the Goal/Question/Metric approach into their existing measurement programs to improve their program performance. Key to their success was the use of feedback sessions as a forum to analyze and interpret measurement data. The paper discusses the elements of the GQM approach and fitting GQM to a measurement program.

/pdf/adopting-gqm-based-measurement-in-an-industrial-environment-3s2qduovoc.pdf

Adopting GQM based measurement in an industrial environment

This document offers a concise introduction to the Goal Question Metric Paradigm (GQM Paradigm), and surveys research on applying and extending the GQM Paradigm. We describe the GQM Paradigm in terms of its basic principles, techniques for structuring GQM-related documents, and methods for performing tasks of planning and implementing a measurement program based on GQM. We also survey prototype software tools that support applying the GQM Paradigm in various ways. An annotated bibliography lists sources that document experience gained while using the GQM Paradigm and offer in-depth information about the GQM Paradigm.

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Technology Package for the Goal Question Metric Paradigm

Software development organizations are beginning to recognize that measurement is a prerequisite for systematic process improvement, and have started to measure their products and processes in order to understand, analyze, plan, and guide their projects. Successful measurement requires a solid understanding of the products, processes, and resources to be measured, an understanding which can only be gained via explicit models. In the MVP Project we are integrating the G/Q/M measurement paradigm with the MVP-L process modeling language in order to guide teams of software developers. This integrated approach is supported by a prototype system, MVP-S, a process-sensitive software engineering environment which offers advanced project guidance using role definitions and measurement data. We motivate the need for measurement, sketch an integration of the measurement and modeling approaches, and demonstrate how MVP-S improves the quality of guidance using measurement.

The Use of Roles and Measurement to Enact Project Plans in MVP-S

All software projects are experiments in the sense of the scientific method because the outcomes are not known in advance, theories and hypotheses may be tested in the project empirically, and data must be collected and analyzed. Empirical data allows us to characterize projects, gauge improvements in an environment, and reason about the value of new technologies. The paper discusses our ideas about practical support for collecting empirical data using a process-sensitive software engineering environment.

Data collection in a process-sensitive software engineering environment

The Software Technology Transfer Initiative Kaiserslautern (STTI-KL) is an organization for transferring software technology, and is sponsored by the Ministry for Commerce and Transport of the state of Rhineland-Palatinate, Germany. The STTI-KL is a partner especially for small and medium size companies to increase the quality and productivity of their software developments. Acknowledgements The authors want to thank all who contributed this document. The described process model is based on the experiences of the partners gained in the ESSI-project CEMP to a great extent. For the exchange of experiences we would like to thank Helmut Woda from

Barbara Hoisl

Papers

Adopting GQM based measurement in an industrial environment

Technology Package for the Goal Question Metric Paradigm

The Use of Roles and Measurement to Enact Project Plans in MVP-S

Data collection in a process-sensitive software engineering environment

A Process Model for GQM-Based Measurement