research-article

A Systematic Review on Software Project Scheduling and Task Assignment Approaches

Authors:

Taskeen Fatima,

Muhammad Waseem Anwar,

Yawar RasheedAuthors Info & Claims

ICCAI '20: Proceedings of the 2020 6th International Conference on Computing and Artificial Intelligence

Pages 369 - 373

https://doi.org/10.1145/3404555.3404588

Published: 20 August 2020 Publication History

Abstract

Software Project Scheduling and Task Assignment are important integral aspects of software project management and contributes to the overall success of software projects. Key objective of Task scheduling/ assignment is to minimize the cost and time of the project. This article i.e. a systematic literature review, is in-fact the first of its kind, conducted in the context of task scheduling and assignment in software industry. This study specifically elaborates the models used in task assignment and summarizes the techniques/ machine learning algorithms to solve the software project scheduling problem (SPSP). Our Initial search brought out 1100 research articles. However, after applying the inclusion and exclusion criteria, 23 most relevant researches were segregated and thereafter thoroughly reviewed. The review revealed that there are 2 types of basic models of Task Scheduling i.e. static and dynamic, however, static models are most widely used. For Task Scheduling, evolutionary algorithms, whereas, for Task Assignment, Support Vector Machine (SVM) algorithms are most widely used. Due to lack of real-world data in software projects, most of the researches utilized synthetic data sets for Task Assignment. Exploring the Task Assignment tools during the course of review process, 7 tools were identified, however, TAMRI has been graded as most efficient.

References

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Cited By

Mohammad AChirchir B(2024)Challenges of Integrating Artificial Intelligence in Software Project Planning: A Systematic Literature ReviewDigital10.3390/digital40300284:3(555-571)Online publication date: 29-Jun-2024
https://doi.org/10.3390/digital4030028
Rankovic NRanković DIvanovic MLazić LRankovic NRanković DIvanovic MLazić L(2024)Use of AI Methods in Software Project SchedulingRecent Advances in Artificial Intelligence in Cost Estimation in Project Management10.1007/978-3-031-76572-8_3(123-155)Online publication date: 7-Dec-2024
https://doi.org/10.1007/978-3-031-76572-8_3
Strang KVajjhala N(2023)Mining Project Failure Indicators From Big Data Using Machine Learning Mixed MethodsInternational Journal of Information Technology Project Management10.4018/IJITPM.31722114:1(1-24)Online publication date: 3-Feb-2023
https://doi.org/10.4018/IJITPM.317221
Show More Cited By

Index Terms

A Systematic Review on Software Project Scheduling and Task Assignment Approaches
1. Software and its engineering
  1. Software creation and management
    1. Software development process management

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ICCAI '20: Proceedings of the 2020 6th International Conference on Computing and Artificial Intelligence

April 2020

563 pages

ISBN:9781450377089

DOI:10.1145/3404555

Copyright © 2020 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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University of Tsukuba: University of Tsukuba

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Published: 20 August 2020

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ICCAI '20

ICCAI '20: 2020 6th International Conference on Computing and Artificial Intelligence

April 23 - 26, 2020

Tianjin, China

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Cited By

Mohammad AChirchir B(2024)Challenges of Integrating Artificial Intelligence in Software Project Planning: A Systematic Literature ReviewDigital10.3390/digital40300284:3(555-571)Online publication date: 29-Jun-2024
https://doi.org/10.3390/digital4030028
Rankovic NRanković DIvanovic MLazić LRankovic NRanković DIvanovic MLazić L(2024)Use of AI Methods in Software Project SchedulingRecent Advances in Artificial Intelligence in Cost Estimation in Project Management10.1007/978-3-031-76572-8_3(123-155)Online publication date: 7-Dec-2024
https://doi.org/10.1007/978-3-031-76572-8_3
Strang KVajjhala N(2023)Mining Project Failure Indicators From Big Data Using Machine Learning Mixed MethodsInternational Journal of Information Technology Project Management10.4018/IJITPM.31722114:1(1-24)Online publication date: 3-Feb-2023
https://doi.org/10.4018/IJITPM.317221
Bahroun ZTanash MAs’ad RAlnajar M(2023)Artificial Intelligence Applications in Project Scheduling: A Systematic Review, Bibliometric Analysis, and Prospects for Future ResearchManagement Systems in Production Engineering10.2478/mspe-2023-001731:2(144-161)Online publication date: 3-May-2023
https://doi.org/10.2478/mspe-2023-0017
Bugayenko YBakare ACheverda AFarina MKruglov APlaksin YPedrycz WSucci G(2023)Prioritizing tasks in software development: A systematic literature reviewPLOS ONE10.1371/journal.pone.028383818:4(e0283838)Online publication date: 6-Apr-2023
https://doi.org/10.1371/journal.pone.0283838
Strang KRao Vajjhala N(2022)Applying machine learning methods to understand unstructured information system big data2022 17th Iberian Conference on Information Systems and Technologies (CISTI)10.23919/CISTI54924.2022.9820116(1-6)Online publication date: 22-Jun-2022
https://doi.org/10.23919/CISTI54924.2022.9820116

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