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Utilizing machine learning algorithms for task allocation in distributed agile software development.

Dimah Al-Fraihat1, Yousef Sharrab2, Abdel-Rahman Al-Ghuwairi3

  • 1Department of Software Engineering, Faculty of Information Technology, Isra University, 11622, Amman, Jordan.

Heliyon
|November 18, 2024
PubMed
Summary
This summary is machine-generated.

Machine learning effectively assigns tasks in distributed agile software development (DASD). Random Forest achieved 96.7% accuracy, improving efficiency and preventing project failures.

Keywords:
DASDDistributed agile software developmentMachine learning (ML)Software engineeringSoftware project managementTask allocation

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Area of Science:

  • Computer Science
  • Software Engineering

Background:

  • Distributed agile software development (DASD) is increasingly adopted globally.
  • Effective task allocation is critical to mitigate risks like project failure and client dissatisfaction.
  • Coordination and communication challenges arise in DASD due to global talent sourcing and cost reduction.

Purpose of the Study:

  • To apply machine learning (ML) predictive algorithms for optimal task-to-role assignment in DASD.
  • To aid software managers in enhancing the efficiency and effectiveness of task allocation.
  • To address coordination and communication challenges inherent in DASD.

Main Methods:

  • Dataset preprocessing involved cleaning, normalization, and partitioning into training, validation, and test sets.
  • Four ML classifiers were evaluated: Random Forest, Decision Tree, K-Nearest Neighbors (K-NN), and AdaBoost.
  • Performance was assessed based on predictive accuracy for task allocation.

Main Results:

  • Random Forest demonstrated superior performance with 96.7% accuracy in task allocation prediction.
  • K-Nearest Neighbors (K-NN) achieved 94.2% accuracy.
  • Decision Tree and AdaBoost showed comparable results with 93.5% and 93% accuracy, respectively.

Conclusions:

  • ML models are highly effective in resolving task allocation complexities within DASD environments.
  • The study validates the potential of ML for optimizing resource management in distributed software projects.
  • The promising outcomes suggest broader applicability of ML in improving DASD project success rates.