DOCSLIB.ORG

Software Measurement Body of Knowledge 57 02 58

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Software Measurement Body of Knowledge 57 02 58 01 Software Measurement Body of Knowledge 57 02 58 03 59 04 60 Alain Abran 05 61 Alain April 06 62 Software Engineering and IT Department, ETS (E´cole de technologie supe´rieure) University, 07 63 Montreal, Quebec, Canada 08 64 09 Luigi Buglione 65 10 66 Industry and Services Business Unit, Engineering.IT SPA, Rome, Italy 11 67 12 68 13 69 14 Abstract 70 15 Measurement is fundamental to sciences and to the engineering disciplines. In the 2004 version of the Guide 71 16 to the Software Engineering Body of Knowledge—the SWEBOK Guide—the software measurement topic is 72 17 dispersed throughout the Guide and discussed in every knowledge area. To facilitate and improve teaching 73 18 and use of measurement in software engineering, an integrated and more comprehensive view of software 74 19 measurement has been built in the form of a software measurement body of knowledge. This entry presents 75 20 this integrated view on software measurement. In the 2010 version of the SWEBOK Guide, it has been 76 21 proposed that software measurement be assigned its own knowledge area. 77 22 78 23 79 24 INTRODUCTION associated with it. Appropriate references are also given 80 25 for each of the topics. Fig. 1 presents a graphical represen- 81 26 Measurement is fundamental to sciences and to the engi- tation of the top-level decomposition of the breakdown of 82 27 neering disciplines. Similarly, the importance of measure- measurement topics. 83 28 ment and its role in better management practices is widely 84 29 acknowledged. Effective measurement has become one of 85 30 the cornerstones of increasing organizational maturity. BASIC CONCEPTS 86 31 Measurement can be performed to support the initiation 87 32 of process implementation and change or to evaluate the This section presents the foundations of software measure- 88 33 consequences of process implementation and change, or it ment, the main definitions, concepts, as well as software 89 34 can be performed on the product itself. information models. 90 35 At the inception of the Software Engineering Body of 91 [1] 36 Knowledge project, measurement activities had been Foundations 92 37 assigned to all the Knowledge Areas (KA) associate editors 93 38 as a criterion for identifying relevant measurement-related In sciences and engineering disciplines, it is the domain of 94 39 knowledge in their respective KA. It is therefore a common knowledge referred to as “metrology” that focuses on the 95 40 theme present across most of the software engineering activ- development and use of measurement instruments and 96 41 ities. However, no attempt had been done to ensure that the measurement processes. The international consensus on 97 42 measurement-related topic had had a full coverage. This entry metrology is documented in the ISO vocabulary of basic 98 [2] 43 presents both an integrated view of the measurement-related and general terms in metrology. The ISO vocabulary 99 44 knowledge spread throughout all other chapters of the defines 144 measurement-related terms. They are orga- 100 45 SWEBOK as well as an improved coverage of the information nized into the following five categories: 101 46 about measurement, as it is actually understood in software 102 47 engineering. The information presented here on software 1. Quantities and units 103 48 measurement has been proposed for inclusion in the 2010 2. Measurements 104 49 version of the Software Engineering Body of Knowledge to 3. Devices for measurement 105 50 be published jointly by the Computer Society and IEEE. 4. Properties of measurement devices 106 51 5. Measurement standards (etalons) 107 52 BREAKDOWN OF TOPICS FOR 108 53 SOFTWARE MEASUREMENT To represent the relationships across the elements of these 109 54 five categories, the classical representation of a production 110 55 The breakdown for software measurement is presented in process is selected, e.g., input, output, and control vari- 111 56 Fig. 1, together with brief descriptions of the major topics ables, as well as the process itself inside. In Fig. 2, the 112 Encyclopedia of Software Engineering DOI: 10.1081/E-ESE-120044182 Copyright # 2011 by Taylor & Francis. All rights reserved. 1 2 Software Measurement Body of Knowledge 01 57 02 58 03 59 04 60 05 61 06 62 07 63 08 64 09 65 10 66 11 67 12 68 13 69 14 70 15 71 16 72 17 73 18 74 19 75 20 76 21 77 22 78 23 79 24 80 25 81 26 82 27 83 28 84 29 85 30 86 31 87 32 88 33 89 34 90 35 91 36 92 37 93 38 94 39 95 40 Fig. 1 Breakdown of topics for the software measurement KA. 96 41 97 42 98 43 output is represented by the “measurement results” and the 99 44 process itself by the “measurement” in the sense of mea- 100 45 surement operations, while the control variables are the 101 46 “etalons” and the “quantities and units.” This set of con- 102 47 cepts then represents the “measuring devices,” and the 103 48 measurement operations are themselves influenced as 104 49 well by the “properties of measurement devices.” 105 50 As in any new domain of application, empirical trials 106 51 represent the starting point to develop a measure, not 107 52 necessarily following a rigorous process. But after a com- 108 53 munity of interest accepts a series of measures to quantify a 109 54 concept (or a set of concepts through a modeling of the 110 55 Fig. 2 Model of the categories of metrology terms in the ISO relationships across the concepts), it is usual to ascertain 111 56 terminology. that the proposed measures are verified and validated. 112 Software Measurement Body of Knowledge 3 01 Definitions and Concepts operations performed on it. It can also be defined 57 02 through the selection of one scale type (ordinal, 58 03 In sciences and engineering disciplines, it is the domain of interval, or ratio). This also includes the defini- 59 04 knowledge referred to as “metrology” that focuses on the tion of units and other allowed composition 60 05 development and use of measurement instruments and operations on the mathematical structure. 61 06 measurement processes. The international consensus on 62 07 metrology is documented in the ISO vocabulary of basic 2. The measurement method: This is a description of the 63 [2] 08 and general terms in metrology. mapping making it possible to obtain a value for a 64 09 The quality of the measurement results (accuracy, given entity. It involves some general properties of 65 10 reproducibility, repeatability, convertibility, random mea- the mapping (mathematical view), together with a col- 66 11 surement errors) is essential for the measurement programs lection of assignment rules (operational description): 67 12 to provide effective and bounded results. Key characteris- 68 13 tics of measurement results and related quality of measur- a. Mapping properties: In addition to the homo- 69 14 ing instruments are defined in the ISO international morphism of the mapping, this can also include 70 15 vocabulary on metrology previously cited. The theory of a description of other mapping properties; for 71 16 measurement establishes the foundation on which mean- instance, a unit axiom (the mandatory associa- 72 17 ingful measurements can be made. The theory of measure- tion of the number 1 with an entity of the empiri- 73 18 ment and scale types is discussed in Ref. [3]. cal set) or, more generally, an adequate selection 74 19 Measurement is defined in the theory as “the assign- of a small finite representative set of elements 75 20 ment of numbers to objects in a systematic way to represent ranked by domain practitioners. 76 21 properties of the object.” An appreciation of software mea- b. Numerical assignment rules: These correspond 77 22 surement scales and the implications of each scale type in to an operational description of the mapping, i.e., 78 23 relation to the subsequent selection of data analysis meth- how to map empirical entities to numerical 79 24 ods are important. values: identification rules, aggregation rules, 80 25 Meaningful scales are related to a classification of procedural modeling of a measurement instru- 81 26 scales: ments family, usage rules, etc. 82 27 83 1. If the numbers assigned are merely to provide labels 28 3. The measurement procedure: This corresponds to a 84 to classify the objects, they are called nominal. 29 complete technical description of the modus operandi 85 2. If they are assigned in a way that ranks the objects 30 of the measurement method in a particular context 86 (e.g., good, better, best), they are called ordinal. 31 (goal, precision, constraints, etc.) and with a particu- 87 3. If they deal with magnitudes of the property relative to 32 lar measuring instrument. 88 a defined measurement unit, they are called interval 33 89 (and the intervals are uniform between the numbers 34 90 unless otherwise specified, and are therefore additive). Verification activities of the design of a software measure 35 91 4. Measurements are at the ratio level if they have an refer to the verification of each of the above design pro- 36 92 absolute zero point, so ratios of distances to the zero ducts of a software measurement method and of its appli- 37 93 point are meaningful. cation in a specific measurement context. The concept of 38 measurement information model (MIM) is presented and 94 39 Software Measurement Methods and Models discussed in the Appendix A of ISO 15939 (see Fig. 3) to 95 40 help in determining what must be specified during mea- 96 41 Software engineers that specialize in measurement should surement planning, performance, and evaluation: 97 42 be able to design a software measurement method when 98 43 required.
Load more

Recommended publications
  • Measuring the Software Size of Sliced V-Model Projects
    2014 Joint Conference of the International Workshop on Software Measurement and the International Conference on Software Process and Product Measurement Measuring the Software Size of Sliced V-model Projects Andreas Deuter PHOENIX CONTACT Electronics GmbH Gregor Engels Dringenauer Str. 30 University of Paderborn 31812 Bad Pyrmont, Germany Zukunftsmeile 1 Email: [email protected] 33102 Paderborn, Germany Email: [email protected] Abstract—Companies expect higher productivity of their soft- But, the “manufacturing” of software within the standard ware teams when introducing new software development methods. production process is just a very short process when bringing Productivity is commonly understood as the ratio of output the binaries of the software to the device. This process is hardly created and resources consumed. Whereas the measurement of to optimize and does not reflect the “production of software” the resources consumed is rather straightforward, there are at all. The creation of the software is namely done in the several definitions for counting the output of a software de- developing teams by applying typical software engineering velopment. Source code-based metrics create a set of valuable figures direct from the heart of the software - the code. However, methods. However, to keep up with the high demands on depending on the chosen process model software developers and implementing new functionality (e.g. for PLC) the software testers produce also a fair amount of documentation. Up to development process within these companies must improve. now this output remains uncounted leading to an incomplete Therefore, they start to analyze their software processes in view on the development output. This article addresses this open detail and to identify productivity drivers.
    [Show full text]
  • Software Error Analysis Technology
    NIST Special Publication 500-209 Computer Systems Software Error Analysis Technology U.S. DEPARTMENT OF COMMERCE Wendy W. Peng Technology Administration Dolores R. Wallace National Institute of Standards and Technology NAT L INST. OF ST4ND & TECH R I.C. NISI PUBLICATIONS A111D3 TTSTll ^QC ' 100 .U57 //500-209 1993 7he National Institute of Standards and Technology was established in 1988 by Congress to "assist industry in the development of technology . needed to improve product quality, to modernize processes, to reliability . manufacturing ensure product and to facilitate rapid commercialization . , of products based on new scientific discoveries." NIST, originally founded as the National Bureau of Standards in 1901, works to strengthen U.S. industry's competitiveness; advance science and engineering; and improve public health, safety, and the environment. One of the agency's basic functions is to develop, maintain, and retain custody of the national standards of measurement, and provide the means and methods for comparing standards used in science, engineering, manufacturing, commerce, industry, and education with the standards adopted or recognized by the Federal Government. As an agency of the U.S. Commerce Department's Technology Administration, NIST conducts basic and applied research in the physical sciences and engineering and performs related services. The Institute does generic and precompetitive work on new and advanced technologies. NIST's research facilities are located at Gaithersburg, MD 20899, and at Boulder, CO 80303.
    [Show full text]
  • Software Metrics: Successes, Failures and New Directions
    Software Metrics: Successes, Failures and New Directions Norman E Fenton Centre for Software Reliability Agena Ltd City University 11 Main Street Northampton Square Caldecote London EC1V OHB Cambridge CB3 7NU United Kingdom United Kingdom Tel: 44 171 477 8425 Tel 44 1223 263753 / 44 181 530 5981 Fax: 44 171 477 8585 Fax: 01223 263899 [email protected] [email protected] www.csr.city.ac.uk/people/norman.fenton/ www.agena.co.uk Abstract The history of software metrics is almost as old as the history of software engineering. Yet, the extensive research and literature on the subject has had little impact on industrial practice. This is worrying given that the major rationale for using metrics is to improve the software engineering decision making process from a managerial and technical perspective. Industrial metrics activity is invariably based around metrics that have been around for nearly 30 years (notably Lines of Code or similar size counts, and defects counts). While such metrics can be considered as massively successful given their popularity, their limitations are well known, and mis-applications are still common. The major problem is in using such metrics in isolation. We argue that it is possible to provide genuinely improved management decision support systems based on such simplistic metrics, but only by adopting a less isolationist approach. Specifically, we feel it is important to explicitly model: a) cause and effect relationships and b) uncertainty and combination of evidence. Our approach uses Bayesian Belief nets which are increasingly seen as the best means of handling decision-making under uncertainty.
    [Show full text]
  • Software Measurement: a Necessary Scientific Basis Norman Fenton
    IEEE TRANSACTIONS ON SOFTWARE ENGINEERING. VOL. 20, NO. 3, MARCH I994 I99 Software Measurement: A Necessary Scientific Basis Norman Fenton Abstruct- Software measurement, like measurement in any 11. MEASUREMENTFUNDAMENTALS other discipline, must adhere to the science of measurement if it is to gain widespread acceptance and validity. The observation In this section, we provide a summary of the key concepts of some very simple, but fundamental, principles of measurement from the science of measurement which are relevant to soft- can have an extremely beneficial effect on the subject. Measure- ware metrics. First, we define the fundamental notions (which ment theory is used to highlight both weaknesses and strengths of are generally not well understood) and then we summarise the software metrics work, including work on metrics validation. We representational theory of measurement. Finally, we explain identify a problem with the well-known Weyuker properties, but also show that a criticism of these properties by Cherniavsky and how this leads inevitably to a goal-oriented approach. Smith is invalid. We show that the search for general software complexity measures is doomed to failure. However, the theory A. What is Measurement? does help us to define and validate measures of specific complexity attributes. Above all, we are able to view software measurement Measurement is defined as the process by which numbers or in a very wide perspective, rationalising and relating its many symbols are assigned to attributes of entities in the real world diverse activities. in such a way as to describe them according to clearly defined Index Terms-Software measurement, empirical studies, met- rules [ 131, [36].
    [Show full text]
  • Ssfm BPG 1: Validation of Software in Measurement Systems
    NPL Report DEM-ES 014 Software Support for Metrology Best Practice Guide No. 1 Validation of Software in Measurement Systems Brian Wichmann, Graeme Parkin and Robin Barker NOT RESTRICTED January 2007 National Physical Laboratory Hampton Road Teddington Middlesex United Kingdom TW11 0LW Switchboard 020 8977 3222 NPL Helpline 020 8943 6880 Fax 020 8943 6458 www.npl.co.uk Software Support for Metrology Best Practice Guide No. 1 Validation of Software in Measurement Systems Brian Wichmann with Graeme Parkin and Robin Barker Mathematics and Scientific Computing Group January 2007 ABSTRACT The increasing use of software within measurement systems implies that the validation of such software must be considered. This Guide addresses the validation both from the perspective of the user and the supplier. The complete Guide consists of a Management Overview and a Technical Application together with consideration of its use within safety systems. Version 2.2 c Crown copyright 2007 Reproduced with the permission of the Controller of HMSO and Queen’s Printer for Scotland ISSN 1744–0475 National Physical Laboratory, Hampton Road, Teddington, Middlesex, United Kingdom TW11 0LW Extracts from this guide may be reproduced provided the source is acknowledged and the extract is not taken out of context We gratefully acknowledge the financial support of the UK Department of Trade and Industry (National Measurement System Directorate) Approved on behalf of the Managing Director, NPL by Jonathan Williams, Knowledge Leader for the Electrical and Software team Preface The use of software in measurement systems has dramatically increased in the last few years, making many devices easier to use, more reliable and more accurate.
    [Show full text]
  • Objectives and Context of Software Measurement, Analysis, and Control Michael A
    Objectives and Context of Software Measurement, Analysis, and Control Michael A. Cusumano Massachusetts Institute of Technology Sloan School WP#3471-92/BPS October 8, 1992 Objectives and Context of Software Measurement, Analysis and Control Michael A. Cusumano Sloan School of Management Massachusetts Institute of Technology Cambridge, Massachusetts USA 02142 Abstract This paper focuses on the what and why of measurement in software development, and the impact of the context of the development organization or of specific projects on approaches to measurement, analysis, and control. It also presents observations on contextual issues based on studies of the major Japanese software producers. 1 Introduction This paper focuses on the what and why of measurement in software development, and the impact of the context of the development organization or of specific projects on approaches to measurement, analysis, and control. It begins by discussing three basic questions: (1) Why do we measure? (2) What is it we want to know? And (3) how does the context affect the ability to measure? The second section presents observations on contextual issues based on my ongoing study of the Japanese [3]. This paper also relies heavily on extensive interviews conducted at Hitachi, Fujitsu, NEC, and Toshiba during May, August, and September 1992. 2 Basic Questions Why we measure aspects of the software development process and conduct analytical exercises or experiments, as well as what we try to do with this information, depends very much on who is doing or requesting the measuring and analysis. There already exists a broad literature on technical aspects software measurement and the relationship to process management, which will not be reviewed here [1, 2, 5, 6].
    [Show full text]
  • 12 Steps to Useful Software Metrics
    12 Steps to Useful Software Metrics Linda Westfall The Westfall Team [email protected] PMB 101, 3000 Custer Road, Suite 270 Plano, TX 75075 972-867-1172 (voice) 972-943-1484 (fax) Abstract: 12 Steps to Useful Software Metrics introduces the reader to a practical process for establishing and tailoring a software metrics program that focuses on goals and information needs. The process provides a practical, systematic, start-to-finish method of selecting, designing and implementing software metrics. It outlines a cookbook method that the reader can use to simplify the journey from software metrics in concept to delivered information. Bio: Linda Westfall is the President of The Westfall Team, which provides Software Metrics and Software Quality Engineering training and consulting services. Prior to starting her own business, Linda was the Senior Manager of the Quality Metrics and Analysis at DSC Communications where her team designed and implemented a corporate wide metric program. Linda has more than twenty-five years of experience in real-time software engineering, quality and metrics. She has worked as a Software Engineer, Systems Analyst, Software Process Engineer and Manager of Production Software. Very active professionally, Linda Westfall is a past chair of the American Society for Quality (ASQ) Software Division. She has also served as the Software Division’s Program Chair and Certification Chair and on the ASQ National Certification Board. Linda is a past-chair of the Association for Software Engineering Excellence (ASEE) and has chaired several conference program committees. Linda Westfall has an MBA from the University of Texas at Dallas and BS in Mathematics from Carnegie- Mellon University.
    [Show full text]
  • Model of Agile Software Measurement: a Case Study Master of Science Thesis in the Programme Software Engineering and Technology
    Model of Agile Software Measurement: A Case Study Master of Science Thesis in the Programme Software engineering and Technology JOHAN GUSTAFSSON Chalmers University of Technology University of Gothenburg Department of Computer Science and Engineering Göteborg, Sweden, June 2011 The Author grants to Chalmers University of Technology and University of Gothenburg the non-exclusive right to publish the Work electronically and in a non-commercial purpose make it accessible on the Internet. The Author warrants that he/she is the author to the Work, and warrants that the Work does not contain text, pictures or other material that violates copyright law. The Author shall, when transferring the rights of the Work to a third party (for example a publisher or a company), acknowledge the third party about this agreement. If the Author has signed a copyright agreement with a third party regarding the Work, the Author warrants hereby that he/she has obtained any necessary permission from this third party to let Chalmers University of Technology and University of Gothenburg store the Work electronically and make it accessible on the Internet. Model of Agile Software Measurement: A Case Study JOHAN GUSTAFSSON © JOHAN GUSTAFSSON, June 2011. Supervisor: ROBERT FELDT Chalmers University of Technology University of Gothenburg Department of Computer Science and Engineering SE-412 96 Göteborg Sweden Telephone + 46 (0)31-772 1000 Department of Computer Science and Engineering Göteborg,Sweden 1 Model of Agile Software Measurement: A Case Study Johan Gustafsson Computer Science and Engineering. Chalmers University of Technology Gothenburg, Sweden [email protected] Abstract— This master thesis examines what type of What companies need is a quick, flexible and cost efficient measurement should be conducted in agile software development.
    [Show full text]
  • An Approach for Deriving, Analyzing, and Organizing Requirements Metrics and Related Information in Systems Projects
    An Approach for Deriving, Analyzing, and Organizing Requirements Metrics and Related Information in Systems Projects Ibtehal Noorwali1, Nazim H. Madhavji1 and Remo Ferrari2 1Computer Science Department, The University of Western Ontario, London, Ontario, Canada 2Siemens Mobility, New York, NY, U.S.A. Keywords: Requirements Metrics, Requirements Management, Measurement Approach. Abstract: Large systems projects present unique challenges to the requirements measurement process: large sets of re- quirements across many sub-projects, requirements existing in different categories (e.g., hardware, interface, software, etc.), varying requirements meta-data items (e.g., ID, requirement type, priority, etc.), to name few. Consequently, requirements metrics are often incomplete, metrics and measurement reports are often unor- ganized, and meta-data items that are essential for applying the metrics are often incomplete or missing. To our knowledge, there are no published approaches for measuring requirements in large systems projects. In this paper, we propose a 7-step approach that combines the use of the goal-question-metric paradigm (GQM) and the identification and analysis of four main RE measurement elements: attributes, levels, metrics, and meta-data items—that aids in the derivation, analysis, and organization of requirements metrics. We illustrate the use of our approach by applying it to real-life data from the rail automation systems domain. We show how the approach led to a more comprehensive set of requirements metrics, improved organization
    [Show full text]
  • Metrics, Models, and Measures for Managing Programs & Projects
    Lean & Agile Performance Measurement Metrics, Models, and Measures for Managing Programs & Projects Dr. David F. Rico, PMP, CSEP, FCP, FCT, ACP, CSM, SAFE, DEVOPS Twitter: @dr_david_f_rico Website: http://www.davidfrico.com LinkedIn: http://www.linkedin.com/in/davidfrico Agile Capabilities: http://davidfrico.com/rico-capability-agile.pdf Agile Cost of Quality: http://www.davidfrico.com/agile-vs-trad-coq.pdf DevOps Return on Investment (ROI): http://davidfrico.com/rico-devops-roi.pdf Dave’s NEW Business Agility Video: https://www.youtube.com/watch?v=-wTXqN-OBzA Dave’s NEWER Development Operations Security Video: https://vimeo.com/214895416 DoD Fighter Jets vs. Amazon Web Services: http://davidfrico.com/dod-agile-principles.pdf Author Background Gov’t contractor with 35+ years of IT experience B.S. Comp. Sci., M.S. Soft. Eng., & D.M. Info. Sys. Large gov’t projects in U.S., Far/Mid-East, & Europe Career systems & software engineering methodologist Lean-Agile, Six Sigma, CMMI, ISO 9001, DoD 5000 NASA, USAF, Navy, Army, DISA, & DARPA projects Published seven books & numerous journal articles Intn’l keynote speaker, 196+ talks to 14,400 people Specializes in metrics, models, & cost engineering Cloud Computing, SOA, Web Services, FOSS, etc. Professor at 7 Washington, DC-area universities 2 On Metrics—Peter Drucker 3 Large TRADITIONAL Projects Size vs. Quality Size vs. Change 16.00 40% 12.80 32% 9.60 24% 6.40 16% CHANGE DEFECTS 3.20 8% 0.00 0% 0 2 6 25 100 400 0 2 6 25 100 400 SIZE SIZE Size vs. Productivity Size vs. Success 5.00 60% 4.00 48% 3.00 36% 2.00 24% 1.00 SUCCESS 12% PRODUCTIVITY 0.00 0% 0 2 6 25 100 400 0 2 6 25 100 400 SIZE SIZE Jones, C.
    [Show full text]
  • Measuring Process Flow Using Metrics in Agile Software Development a Systematic Literature Review and a Case Study
    Master of Science in Software Engineering June 2019 Measuring Process Flow using Metrics in Agile Software Development A Systematic Literature Review and a Case Study Krishna Sai Bitla Sairam Sagar Veesamsetty Faculty of Computing, Blekinge Institute of Technology, 371 79 Karlskrona, Sweden This thesis is submitted to the Faculty of Computing at Blekinge Institute of Technology in partial fulfilment of the requirements for the degree of Master of Science in Software Engineering. The thesis is equivalent to 20 weeks of full-time studies. The authors declare that they are the sole authors of this thesis and that they have not used any sources other than those listed in the bibliography and identified as references. They further declare that they have not submitted this thesis at any other institution to obtain a degree. Contact Information: Author(s): Krishna Sai Bitla E-mail: [email protected] Sairam Sagar Veesamsetty E-mail: [email protected] University advisor: DR. Krzysztof Wnuk Department of Software Engineering Industry Advisor: Dani Benjamin Plant Manager VCEZ at Volvo Cars Faculty of Computing Internet : www.bth.se Blekinge Institute of Technology Phone : +46 455 38 50 00 SE-371 79 Karlskrona, Sweden Fax : +46 455 38 50 57 ii ACKNOWLEDGEMENTS Our Journey in the thesis has been tremendously intense learning, value added and a remarkable experience. First and foremost, we would first like to thank from our bottom of our hearts to our university supervisor, Dr. Krzysztof Wnuk who has always been a third wheel in the thesis study. Whenever we have faced trouble during thesis time he has made sure that his doors were always open for supervisions, comments on the study, suggestions for unique considerations during the thesis period.
    [Show full text]
  • Contextual Injection of Quality Measures Into Software Engineering Processes
    International Journal on Advances in Software, vol 4 no 1 & 2, year 2011, http://www.iariajournals.org/software/ 76 Contextual Injection of Quality Measures into Software Engineering Processes Gregor Grambow and Roy Oberhauser Manfred Reichert Computer Science Dept. Institute for Databases and Information Systems Aalen University, Germany Ulm University, Germany {gregor.grambow, roy.oberhauser}@htw-aalen.de [email protected] Abstract—Despite improvements in software engineering process (e.g., junior vs. senior engineers, information needs), processes and tools, concrete preventative and analytical activity coordination with the overall team process, the software quality assurance activities are still typically contextual and project influences on the processes (e.g., manually triggered and determined, resulting in missed or schedule, resource availability), and software quality untimely quality opportunities and increased project overhead. assurance (SQA) dependencies (e.g., quality plan, reactive Quality goals, when defined, lack holistic environmental quality measures, metric dependencies). support for automated performance measurement and Historically software development projects have also governance that is tightly integrated in the low-level faced difficulties in meeting budget, schedule, functionality, operational software engineering processes. This results in and quality targets [6][7][8]. A more recent study in 2002 by higher quality risks and cost risks. Based on adaptive process the National Institute of Standards and Technology (NIST) management, an approach is presented that injects situationally-determined quality measure proposals into the found that most delivered software products are still stricken concrete workflows of software engineers, using contextual by bugs and defects [9]. While some of these difficulties semantic knowledge and multi-agent quality goal tracking and might be ascribed to a misaligned planning environment in decision-making.
    [Show full text]