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

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 Customers Want


What requirements tell us

What Requirements Tell Us


Standish group standish94

Standish Group [Standish94]

  • Exceeded planned budget by 90%

  • Schedule by 222%

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


Underlying problems

Underlying Problems

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

Scarcity of data


Consequences

Consequences

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


Related research economic models

Related Research: Economic Models


Why are machine learning algorithms not used more often for estimating early in the life cycle

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


Related research 2

Related Research - 2


Using machine learning to predict project effort empirical case studies in data starved domains

Goal

Apply Machine Learning (Neural Network)

early in the software lifecycle

against Empirical Data


Neural network

Neural Network


Using machine learning to predict project effort empirical case studies in data starved domains

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-Based Fleet to B2B


Experiment 1 product results

Experiment 1: Product Results


Experiment 2 project based results fleet to b2b

Experiment 2: Project-Based Results Fleet to B2B


Experiment 3 product based b2b to fleet

Experiment 3: Product-Based B2B to Fleet


Extrapolation issue

Extrapolation issue

Largest SLOCs divided by each other

4398 / 2796 = 1.57


Experiment 3 product results

Experiment 3: Product Results


Experiment 4 project based results b2b to fleet

Experiment 4: Project-Based Results B2B to Fleet


Results

Results


Conclusions

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

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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