https://github.com/anuraganalog/ml-for-software-project-management

Using Machine Learning for software effort
https://github.com/anuraganalog/ml-for-software-project-management

cocomo dataset eda effort engineering estimation jupyter learning machine networks neural notebooks software

Last synced: about 1 month ago
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Using Machine Learning for software effort

• Host: GitHub
• URL: https://github.com/anuraganalog/ml-for-software-project-management
• Owner: AnuragAnalog
• License: gpl-3.0
• Created: 2020-03-23T03:18:35.000Z (almost 5 years ago)
• Default Branch: master
• Last Pushed: 2020-05-30T15:52:18.000Z (over 4 years ago)
• Last Synced: 2025-01-03T23:34:37.683Z (about 1 month ago)
• Topics: cocomo, dataset, eda, effort, engineering, estimation, jupyter, learning, machine, networks, neural, notebooks, software
• Language: Jupyter Notebook
• Size: 1.05 MB
• Stars: 9
• Watchers: 3
• Forks: 3
• Open Issues: 0
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

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README

        
# Machine Learning for software effort Estimation

How Machine Learning can be used in software estimation

## COCOMO Dataset

Software development effort multipliers

| Variable | Description |

| --- | --- |

| RELY | Reliability |

| DATA | Database size |

| CPLX | Application complexity |

| TIME | Restriction of time |

| STOR | Restriction of main store |

| VIRT | Volatility of virtual machine |

| TURN | Time of machine performance |

| ACAP | Analyst capability |

| AEXP | Experience with application |

| PCAP | Programmer capability |

| VEXP | Experience with virtual machine |

| LEXP | Experience with programming language |

| MODP | Use programming modern practice |

| TOOL | Use software tools |

| SCHED | Schedule for development |

| LOC | Lines of Code |

| ACTUAL | Software size |

It is a set of 63 project instances, which has 17 feature columns, out of which 15 are categorical variables, and two are continuous variables.

Of which Lines of code(LOC) is an independent variable and Effort is the actual target.