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.