Automated Software Cost Estimation By James Roberts EEL 6883 Spring 2007

Background Over 53% of software projects overrun by more than 50% in both budget and schedule Software overrun in budget is a failure Software overrun in schedule is a failure Goal of software engineering is to deliver software on time and within budget

Possible Solution Automated Software Cost Estimation – Look at history – Generalize data – Create equations – Parametric

Input Measurements SLOC – Source Lines of Code DSI – Delivered Source Instructions Function Points

Cost Estimation Models COCOMO 81 COCOMO II REVIC SLIM Others

COCOMO Developed by Barry Boehm in 81 Based on historical database DSI is the input Three versions – Basic Model – Intermediate Model – Detailed Model

COCOMO II Updated the COCOMO 81 model Allows for – Spiral development – Rapid prototyping – COTS integration – OO Design Uses SLOC

REVIC Revised Intermediate COCOMO Developed by Ray Kile Updated to use Air Force project data Adds a mode for Ada development Inputs are the same as COCOMO 81

SLIM Software Life-Cycle Model Developed by Larry Putnam Uses a Rayleigh distribution – Project personnel vs. Time Intended for large projects Fewer parameters

QSM’s SLIM Tool Based on the SLIM model Windows based Easy to use Several different wizards for quickly generating an estimate Five steps to create an estimate

Softstar’s CoStar Based on the COCOMO model Windows based Easy to use Many different COCOMO variations Create Estimate Wizard Many parameters required Highly configurable Full featured demo version available

Galorath’s SEER-SEM Based on proprietary COCOMO-like models Windows based Moderately easy to use Create Estimate Wizard Few parameters required up front Highly configurable Poor demo version

Conclusion Would recommend the Softstar CoStar software Software Cost Estimation is important for any program manager These tools are vital to quickly generating estimates for success

References 1.Dave Srulowitz, M.B., Vic Helbling. Software Estimation [cited; Available from: Briand, L.C., et al. An assessment and comparison of common software cost estimation modeling techniques Boehm, B.W., Software Engineering Economics. 1st ed. 1981: Prentice-Hall. 4.COCOMO II. [cited; Available from: Boehm, B.C., B.; Horowitz, E.; Madachy, R.; Shelby, R.; Westland, C. An Overview of the COCOMO 2.0 Software Cost Model. in Software Technology Conference Systems, S. Overview of COCOMO [cited; Available from:

References Cont. 7.C. Abts, B.C., S. Devnani-Chulani, E. Horowitz, R. Madachy, D. Reifer, R. Selby, B. Steece, COCOMO II Model Definition Manual. Technical report, Center for Software Engineering, USC Albrecht, A., Function Points: A New Way of Looking at Tools Parametric Cost Estimating Handbook. US Dept. of Defense, Washington D.C., Agency, D.C.M. DCMA Guidebook - Software Acquisition Management [cited. 11.Boehm, B.A., C.; Chulani, S., Software Development Cost Estimation Approaches - A Survey. Annals of Software Engineering, (1-4): p Chris, F.K., An empirical validation of software cost estimation models. Commun. ACM, (5): p Sultanodlu, S. Software Measurement, Cost Estimation, SLIM, COCOMO [cited; Available from: