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Software Effort Estimation Planning to Meet Schedule Lewis Sykalski 5/01/2010.

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Presentation on theme: "Software Effort Estimation Planning to Meet Schedule Lewis Sykalski 5/01/2010."— Presentation transcript:

1 Software Effort Estimation Planning to Meet Schedule Lewis Sykalski 5/01/2010

2 How Managers View Software Effort Estimation Nebulous Vague No correlation to reality Why bother

3 Effects of Bad/No Estimation Underestimation can lead to Schedule/Cost Over- runs: – Diminished Confidence by upper management – Customer upset – Can affect schedules/cost downstream Overestimation can lead to Schedule/Cost Under- runs: – Adversely affect positioning – De-motivator for employees – More profitable opportunities passed over

4 Effect of Estimation Clearer expectation of level of effort Allows SPM to better allocate resources Helps SPM account for change Shows upper-level management that manager has a plan

5 Abstract Most managers today are unaware of established software effort estimation methodologies or don’t account for unforeseen consequences when using a method. This paper attempts to reconcile this by surveying several effort estimation approaches and gauging both the utility and inherent pitfalls in each. Additionally, this paper will present a refined method for software effort estimation based on expert judgment and describe benefits of using said method.

6 Current Methodologies Expert Judgment Consensus-based Estimation Price-to-Win Analogy Costing Function Point Analysis Algorithmic Models – Cocomo – SLIM – PriceS

7 Expert Judgment Consulting one or more experts Experts leverage their own past experiences or methods Typically arrive at task duration. Sometimes size which can be converted to effort with assumed productivity. Sometimes averaging occurs between multiple experts’ estimates to smooth out results

8 Expert Judgment Utility Advantages: Not much time/effort required Not conceptually difficult Disadvantages: Not repeatable or explicit No consistent rationale for estimates Prone to error and very subjective If estimates do not match size of experts’ historical experiences, estimate can be way off-based Experts with right experience could be hard to find

9 Consensus-based Estimation Logical extension of expert judgment Multiple experts/developers seek to reach consensus on estimate Wideband-Delphi most popular: – Short discussion of to define tasks – Secret ballots – Any deviations must be resolved by discussion & revote Planning Poker (Agile Method) – Card representing duration of task is shown – Resolution follows until consensus is reached

10 Consensus-Based Estimation Utility Advantages: Same advantages as parent -- Expert Judgment Experts have discussed and agreed on the estimate Hidden tasks are often discovered Disadvantages: Same disadvantages as parent – Expert Judgment Amount of time required to reach consensus “Anchoring”: When process is loosened and someone affects groups predispositions. (e.g. “It can’t be more than 20 days…”)

11 Price-to-Win Estimate is estimated at: – whatever the optimum value is in order to win the contract or – whatever funds or time the customer has available

12 Price-to-Win Utility Advantages: Win the contract Effort could contract to fill the difference? Not a lot of time required Disadvantages: Considered poor practice Large discrepancies in effort anticipated and effort required – might result in severe overruns Quality of the product may suffer in trying to reach deadline Profit loss?

13 Analogy Costing Estimates effort by analogizing to past project(s) ∆Effort = difference in project from past project(s) in terms of requirements, reuse opportunity, process, etc.

14 Analogy Costing Utility Advantages: Grounded in past history Full effort need not be estimated only delta Not a lot of time required (just meaningful analysis of deltas) Disadvantages: Meaningful data may not be present Subjectivity in deltas Past historical data may not be representative (innovation efforts) Abnormal conditions in past projects (that estimator is unaware of) may throw off results

15 Function Point Analysis (Albrecht) Function point represents a piece of functionality of the program: – User-input – User-output – Inquiry (interactive inputs requiring response) – External (files shared or used externally) – Internal (files shared or used internally)

16 Function Point Analysis (Albrecht) where, i=type of FP (User-input, output, etc) j=Complexity level of FP (1-3) Nij is the number of FPs of type ij Wij is the weight of the FPs of type ij where a & b come from historical data/curve-fitting Effort = LOC*Productivity

17 Function Point Analysis Utility Advantages: Can be formulated at requirement time very early in the software-lifecycle Provides good traceability in mapping tasks to effort No subjectivity is required as to the task duration Less prone to subjective bias than expert judgment based techniques Disadvantages: Detailed requirements & consensus on complexity is required Very nebulous Time involved to arrive at such an estimate Requires a good handle up-front of the requirements (prone to requirements creep/hidden requirements)

18 Algorithmic Models Where, {C1, C2, …, Cn} denote cost factors F represents the function used

19 COCOMO (Boehm) Regression Formula Historical Data Inputs Current Project Characteristics: (Nominal 1.0) – Product: Reliability, Documentation Needs, etc. – Computer: Performance Constraints, Volatility, etc – Personnel: Capability, Familiarity w/language, etc – Project: Co-location, Tools productivity gains, etc Project Categorization: (Different historical data) – Organic: Small team / flexible process – Embedded: Large team / tight process – Semi-Detached: Somewhere in-between

20 SLIM (Putnam) Spent much time doing curve fitting, came up with following equation: where, Size is ESLOC or Effective SLOC (new+modified) B is a scaling factor indicative of project size Productivity is the Process Productivity factor Time is duration of project schedule (years)

21 PriceS Parametric cost-estimation system Can accept a wide variety of inputs: – Use-Cases, Function Points, Predictive Object Points, Functional Size, SLOC, and Fast Function Points. Effort estimates also factor in: – Software Lifecycle processes (Waterfall, Agile, etc.) – Software Language Choice (Java, C++, etc).

22 Algorithmic Models Utility Advantages: Objective Repeatable results Historical data often represents MANY past projects Disadvantages: Complex formulas are hard to comprehend Requires faith on the users’ end Subjective historical data Subjective Cost factors may throw off equations May require difficult/time consuming tailoring using estimator’s historical data

23 New Model: Predictive Expert Judgment Combines inherent simplicity of expert judgment method w/feedback control provided for in other models Requires: Diligent tracking of actual times for past tasks Records of experts’ estimates toward those tasks.

24 Predictive Expert Judgment (cont.) Steps: Solicit effort estimates for each task from each expert As tasks are completed build a repository tracking how close the experts’ estimate was to past historical estimates Weight each experts’ future estimate by how well he has historically estimated

25 Predictive Expert Judgment Equation Where, – Wi corresponds to each expert’s trust weight based on historical performance – Ei corresponds to each experts’ estimate for the current task being estimated

26 Predictive Expert Judgment (cont.) Wi can be calculated: – Simple formula involving standard deviations – Intermediate custom formula where one disregards some experts based if they are outside target range or based on external factors – Weighted Variance Equation

27 Simple Formula Where, – Wi corresponds to each expert’s trust weight based on historical performance – N is the number of experts with historical data – σi is the current experts’ historical stdev – σn is each experts’ historical stdev

28 Expert Judgment Example Expert A: 40 hours Expert B: 20 hours Expert C: 5 hours Expert D: 20 hours Expert E: 30 hours No historical data: 40*0.2+20*0.2+5*0.2+20*0.2+30*0.2 =23.0 hours

29 Predictive Expert Judgment Example 0.35*40+0.16*20+0.11*5+0.22*20+0.17*30=27.0 hours Everybody counts methodology…

30 Predictive Expert Judgment – W/Constraints If we had rules where we threw out experts’ estimates if they were wildly off: > 12.0 STDEV 0.47*40+0.29*20+0.23*30=31.8 hours (Could be closer?) (Where σi <12.0)

31 Predictive Expert Judgment – W/Constraints (cont.) You could alternatively tighten the standard deviation constraint to trust only the leading expert… 1.0*40=40.0 hours (Where σi = best)

32 Predictive Expert Judgment – W/Constraints (cont.) You could also adjust for deviations in estimate (how far they are normally off and in what direction) 0.35*38.75+0.16*20+0.11*3.75+0.22*13.75+0.17*26.25=24.4 hours

33 Results & Analysis No model can estimate the cost of software with a high degree of accuracy Expert Judgment was found to be just as good as algorithmic models (in 15 case studies) Uncertainty and Probability should be added to most models More historical data needs to be collected from industry

34 Conclusion A software manager who takes time to perform and reconcile software effort estimation will be on safer footing than a manager who doesn’t Use the advantages/disadvantages in paper and use one you feel most comfortable with

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