OXFORD SOFTWARE ENGINEERING Software Engineering Services & Consultancy Slide 1© OSEL 2008 CMMI and Metrics BCS SPIN SG, 19 February 2008 Clifford Shelley OXFORD SOFTWARE ENGINEERING Ltd 9 Spinners Court, 53 West End, Witney, Oxfordshire OX28 1NH Tel. +44 (0)

OXFORD SOFTWARE ENGINEERING Software Engineering Services & Consultancy Slide 2© OSEL 2008 Objective 1.Present the background and history of measurement within CMMI 2.Place measurement within the context of CMMI (or CMMI within the context of measurement?) 3.Identify common issues and concerns with measurement within the context of CMMI, and their resolution 4.Look at some measurement approaches that can be really useful (it is assumed here that ‘measurement’ is equivalent to ‘metrics’)

OXFORD SOFTWARE ENGINEERING Software Engineering Services & Consultancy Slide 3© OSEL 2008 Background… Original SW CMM (a tool to measure software capability) –Included measurement and analysis as a ‘common feature’ – briefly described –Expected product size estimates in planning –Measurement of processes (problematic at L2, expected at L3) –SPC like KPA at L4

OXFORD SOFTWARE ENGINEERING Software Engineering Services & Consultancy Slide 4© OSEL 2008 …Background… Carried forward and developed to CMMI Staged Representation –ML2 Measurement and Analysis is a PA in its own right –NB: Includes GQM as SP PP requires product sizing –ML3 OPD SP process data defined and collected (using MA) IPM SP 1.2 – use historical data for estimating –ML4 SPC expectations (OPP) Control common causes of variation? Exploited by QPM –ML5 Quantitiative Process Improvement – pervasive measurement CAR uses measurement as analysis tool OID uses measurement as an analysis tool

OXFORD SOFTWARE ENGINEERING Software Engineering Services & Consultancy Slide 5© OSEL 2008 …Background Continuous Representation – CL2: All PAs expected to be monitored and controlled (GP2.8 – measurement is expected) –CL3: Standard process measures [defined and] stored (GP3.2) –CL4: Quantitative objectives for processes established (GP4.1) SPC required - stabilize sub-process performance (GP4.2) –Control special causes of variation – part of process capability as understood by production engineering –CL5: Establish quantitative process improvement (GP5.1 sub-practice) Manage common causes too – to enable process capability

OXFORD SOFTWARE ENGINEERING Software Engineering Services & Consultancy Slide 6© OSEL 2008 Measurement within CMMI… MA PA is the enabler “…is to develop and sustain a measurement capability that is used to support management information needs” Interpretation?

OXFORD SOFTWARE ENGINEERING Software Engineering Services & Consultancy Slide 7© OSEL 2008 …Measurement within CMMI… MA Scope –SG1 Align Measurement and Analysis Activities SP 1.1 Establish Measurement Objectives SP 1.2 Specify Measures SP 1.3 Specify Data collection and Storage Procedures SP 1.4 Specify Analysis Procedures –SG2 Provide Measurement Results SP 2.1 Collect Measurement Data (includes verification) SP 2.2 Analyze Measurement Data SP 2.3 Store Data and Results SP 2.4 Communicate Results (to aid decision making)

OXFORD SOFTWARE ENGINEERING Software Engineering Services & Consultancy Slide 8© OSEL 2008 …Measurement within CMMI MA PA – Applicability for ML2 –“…support management information needs…” –Project management (initially) –“…at multiple levels in the organization…” –and process management (GP 2.8) –and products (product components provided by suppliers) Not considered explicitly –testing? – testers tend to be a measurement ‘centre of excellence’ –development? – developers don’t (design not amenable to measurement?)

OXFORD SOFTWARE ENGINEERING Software Engineering Services & Consultancy Slide 9© OSEL 2008 Practical concerns 1: Fear –‘I don’t want to be measured’ It is abstract and can be difficult (Pfleeger) –Distinguish between metrics designers and metrics users –and train accordingly Where does it fit? –Tactical capability or organizational infrastructure, or mix?

OXFORD SOFTWARE ENGINEERING Software Engineering Services & Consultancy Slide 10© OSEL 2008 Practical concerns 2: What should we measure? –RTFM –SG1 –Ask what do you* need to know? Why? Who monitors and controls (measures)processes –At ML2, at ML3? Who monitors and controls the MA process? Metrics Repository –Central/organizaton, or local/project * ‘you’, perhaps ‘we’, but not ‘they’

OXFORD SOFTWARE ENGINEERING Software Engineering Services & Consultancy Slide 11© OSEL 2008 Common Circumstances 1: Product sizing rarely done, or done well –Difficulty with identifying entities and their quantitative attributes –Fix: analysts extract identities, counts and attributes of system elements from developer/estimators – start with task based estimation spreadsheet and work upstream, ‘What were they thinking?’ Organizational Measurement Infrastructure in place –Collects lots of data (if it moves measure it) –No traceability back to objectives (SG1 missing) data is orphaned from rationale – can’t reuse –Fix: discard collected data without definitions or rationale, then reverse engineer objectives (SG1) for existing data collection systems – usually results in opportunity to shed data collection activity – reduce costs – although rarely taken up Organizational measurement data is unverified – of unknown accuracy –used for admin/billing –Not credible, known to be invalid (timesheets) –Not used by collectors –Data is for reporting, not using –Fix: verify data – presumes SG1

OXFORD SOFTWARE ENGINEERING Software Engineering Services & Consultancy Slide 12© OSEL 2008 Common Circumstances 2: Good measurement –Developed, owned and used locally, within teams –Can be undervalued (seems obvious) –MA SG1 implicit There is a limited ‘information horizon’ –Visibility is limited, and may be better that way –Measurement data doesn’t travel well –‘Drill down’ is limited – even if it looks like it isn’t

OXFORD SOFTWARE ENGINEERING Software Engineering Services & Consultancy Slide 13© OSEL 2008 Good Measurement 1: 1.Purpose clear and understood by collectors, analysts and decision makes 2.Measures are defined (not just described) 3.Data collectors are users (short feedback loops) 4.Accuracy and validity known (as minimal requirement) 5.Can stop collecting when no longer needed

OXFORD SOFTWARE ENGINEERING Software Engineering Services & Consultancy Slide 14© OSEL 2008 Good Measurement 2: 1.KISS –Minimal arithmetic, especially multiplication and division –(this includes percentages) –Arithmetic obscures much, reveals little 2.Non parametric approaches –Robust, widely applicable to ‘messy’ software engineering data –Not the usual statistical approach 3.Consider ‘Exploratory Data Analysis’ (EDA) –Tukey 4.Use Graphics But not pie charts –Let data show its information content – patterns, trends, outliers –Tufte 5.GQMG tutu –Goal Question Metric, Graphics – guided by Tufte and Tukey –SEI GQ(I)M 6.SPC –Later, much later –SPC Rule #1, know what you’re doing

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OXFORD SOFTWARE ENGINEERING Software Engineering Services & Consultancy Slide 16© OSEL 2008 O X F O R D S O F T W A R E E N G I N E E R I N G L I M I T E D 9 Spinners Court, 53 West End, Witney, Oxfordshire OX28 1NH Tel. +44 (0)

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OXFORD SOFTWARE ENGINEERING Software Engineering Services & Consultancy Slide 18© OSEL 2008 Supplementary Material…

OXFORD SOFTWARE ENGINEERING Software Engineering Services & Consultancy Slide 19© OSEL 2008 Empirical relational system

OXFORD SOFTWARE ENGINEERING Software Engineering Services & Consultancy Slide 20© OSEL 2008 Empirical relational system Formal relational system measurement real worldmathematical world

OXFORD SOFTWARE ENGINEERING Software Engineering Services & Consultancy Slide 21© OSEL 2008 Empirical relational system Formal relational system Results measurement mathematics and statistics real worldmathematical world

OXFORD SOFTWARE ENGINEERING Software Engineering Services & Consultancy Slide 22© OSEL 2008 Empirical relational system Formal relational system Results measurement interpretation mathematics and statistics real worldmathematical world Relevant empirical information

OXFORD SOFTWARE ENGINEERING Software Engineering Services & Consultancy Slide 23© OSEL 2008 Empirical relational system Formal relational system Results measurement interpretation mathematics and statistics decisions and actions real worldmathematical world Relevant empirical information From Pfleeger 1998

OXFORD SOFTWARE ENGINEERING Software Engineering Services & Consultancy Slide 24© OSEL 2008 Empirical relational system Formal relational system Results refined measurement improved interpretation mathematics and statistics better decisions and actions real worldmathematical world Relevant empirical information

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OXFORD SOFTWARE ENGINEERING Software Engineering Services & Consultancy Slide 26© OSEL 2008 ‘Anscombe’s Quartet’ – American Statistician 1973

OXFORD SOFTWARE ENGINEERING Software Engineering Services & Consultancy Slide 27© OSEL 2008 N = 11 Mean of X’s = 9.0 Mean of Y’s = 7.5 Regression line = Y + 0.5X Standard error of estimate of slope = t = 4.24 Sum of squares X – X = Regression of sum of squares = Residual sum of squares of Y = Correlation coefficient = 0.82 R 2 = 0.67 _

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OXFORD SOFTWARE ENGINEERING Software Engineering Services & Consultancy Slide 30© OSEL 2008 Process Capability: Indices for measuring process goodness Cp = USL - LSL / 6  or 2T / 6  –Cp < 1 process is incapable –Cp > 1 process is capable (6  processes have Cp of 2) –does not account for process drift so... Cpk = the lesser of (USL - X) / 3  or (X - LSL) / 3  ==

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