Using machine learning to predict project effort empirical case studies in data starved domains
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Using Machine Learning to Predict Project Effort: Empirical Case Studies in Data-starved Domains. Gary D. Boetticher Department of Software Engineering University of Houston - Clear Lake. What Customers Want. What Requirements Tell Us. Standish Group [Standish94].

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Using Machine Learning to Predict Project Effort: Empirical Case Studies in Data-starved Domains

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Using Machine Learning to Predict Project Effort: Empirical Case Studies in Data-starved Domains

Gary D. Boetticher

Department of Software Engineering

University of Houston - Clear Lake


What Customers Want


What Requirements Tell Us


Standish Group [Standish94]

  • Exceeded planned budget by 90%

  • Schedule by 222%

  • More than 50% of the projects had less than 50% requirements


Underlying Problems

85% are at CMM 1 or 2 [CMU CMM95, Curtis93]

Scarcity of data


Consequences

Early life-cycle estimates use a factor of 4 [Boehm81, Heemstra92]


Related Research: Economic Models


Why are Machine Learning algorithms not used more often for estimating early in the life cycle?


Related Research - 2


Goal

Apply Machine Learning (Neural Network)

early in the software lifecycle

against Empirical Data


Neural Network


Data

  • B2B Electronic Commerce Data

    • Delphi-based

    • 104 Vectors

  • Fleet Management Software

    • Delphi-based

    • 433 Vectors


Experiment 1: Product-Based Fleet to B2B


Experiment 1: Product Results


Experiment 2: Project-Based Results Fleet to B2B


Experiment 3: Product-Based B2B to Fleet


Extrapolation issue

Largest SLOCs divided by each other

4398 / 2796 = 1.57


Experiment 3: Product Results


Experiment 4: Project-Based Results B2B to Fleet


Results


Conclusions

  • Bottom-up approach produced very good results on a project-basis

  • Results comparable between NN and stat.

  • Scaling helped

  • Estimation Approach is suitable for Prototype/Iterative Development


Future Directions

  • Explore an extrapolation function

  • Apply other ML algorithms

  • Collect additional metrics

  • Integrate with COCOMO II

  • Conduct more experiments (additional data)


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