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A novel quantitative evaluation approach for software project schedules using statistical model checking

Published: 31 May 2014 Publication History

Abstract

Project schedules are essential for successfully carrying out software projects. To support manager’s decision making, many project scheduling algorithms have been developed in recent years for generating candidate project schedules. However, these project schedules may not be able to be used directly because the uncertainty and complexity of real-world software development environments which have been overlooked or simplified in the project scheduling algorithms. Therefore, significant human efforts are still required to evaluate and compare these project schedules. To address such a problem, we propose a quantitative analysis approach based on statistical model checking technique which serves as a novel evaluation method for project schedules. By using the UPPAAL-SMC, we can systematically evaluate the performance of a project schedule and answer complex questions which are vital for manager’s decision making but cannot be efficiently addressed by any existing tools. The preliminary results show that our approach can efficiently filter out unsatisfactory candidates by answering simple “yes or no” questions first and then help effectively compare the rest by answering complicated user specified questions. Therefore, the human efforts in planning project schedules can be significantly reduced.

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cover image ACM Conferences
ICSE Companion 2014: Companion Proceedings of the 36th International Conference on Software Engineering
May 2014
741 pages
ISBN:9781450327688
DOI:10.1145/2591062
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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  • TCSE: IEEE Computer Society's Tech. Council on Software Engin.

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Association for Computing Machinery

New York, NY, United States

Publication History

Published: 31 May 2014

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Author Tags

  1. Project Schedule
  2. Quantitative Evaluation
  3. Statistical Model Checking

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Cited By

View all
  • (2023)stohMCharts: A Modeling Framework for Quantitative Performance Evaluation of Cyber-Physical-Social SystemsIEEE Access10.1109/ACCESS.2023.327267211(44660-44671)Online publication date: 2023
  • (2022)Uncertainty-Aware Behavior Modeling and Quantitative Safety Evaluation for Automatic Flight Control Systems2022 IEEE 22nd International Conference on Software Quality, Reliability and Security (QRS)10.1109/QRS57517.2022.00062(549-560)Online publication date: Dec-2022
  • (2020)Statistical Model Checking-Based Evaluation and Optimization for Cloud Workflow Resource AllocationIEEE Transactions on Cloud Computing10.1109/TCC.2016.25860678:2(443-458)Online publication date: 1-Apr-2020
  • (2020)Quantitative Timing Analysis for Cyber-Physical Systems using Uncertainty-Aware Scenario-Based SpecificationsIEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems10.1109/TCAD.2020.3012843(1-1)Online publication date: 2020
  • (2016)Quantitative timing analysis of UML activity diagrams using statistical model checkingProceedings of the 2016 Conference on Design, Automation & Test in Europe10.5555/2971808.2971987(780-785)Online publication date: 14-Mar-2016
  • (2016)Quantitative Analysis of Variation-Aware Internet of Things Designs Using Statistical Model Checking2016 IEEE International Conference on Software Quality, Reliability and Security (QRS)10.1109/QRS.2016.39(274-285)Online publication date: Aug-2016
  • (2015)Evaluating Energy Consumption for Cyber-Physical Energy SystemProceedings of the 2015 IEEE 39th Annual Computer Software and Applications Conference - Volume 0210.1109/COMPSAC.2015.114(5-14)Online publication date: 1-Jul-2015
  • (2014)Variation-Aware Resource Allocation Evaluation for Cloud Workflows Using Statistical Model CheckingProceedings of the 2014 IEEE Fourth International Conference on Big Data and Cloud Computing10.1109/BDCloud.2014.48(201-208)Online publication date: 3-Dec-2014

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