Maintenance strategy optimization using a continuous-state partially observable semi-Markov decision process

2011 ◽  
Vol 51 (2) ◽  
pp. 300-309 ◽  
Author(s):  
Yifan Zhou ◽  
Lin Ma ◽  
Joseph Mathew ◽  
Yong Sun ◽  
Rodney Wolff
Keyword(s):  
Markov Decision Process ◽  
Decision Process ◽  
Maintenance Strategy ◽  
Continuous State ◽  
Markov Decision ◽  
Partially Observable

scholarly journals Partially observable Markov Decision Process to prioritize software defects

2021 ◽  
Author(s):  
Shirin Akbarinasaji
Keyword(s):  
Markov Decision Process ◽  
Decision Process ◽  
Tracking System ◽  
Dependency Graph ◽  
Relative Importance ◽  
Bug Reports ◽  
Markov Decision ◽  
Partially Observable Markov ◽  
Partially Observable ◽  
Issue Tracking System

Background: Bug tracking systems receive many bug reports daily. Although the software quality team aims to identify and resolve these bugs, they are never able to fix all of the reported bugs in the issue tracking system before the release deadline. However, postponing the bug fixing may have some consequences. Prioritization of bug reports will help the software manager decide which bugs to fix and which bugs to postpone. Typically, bug reports are prioritized based on the severity, priority, time and effort for fixing, customer pressure, etc. Aim: Previous studies have shown that these factors may not be appropriate for prioritization. Therefore, relying on them to automate bug prioritization might be misleading. In this dissertation, we aim to prioritize bug reports with respect to the consequence of not fixing the bugs in terms of their relative importance in the issue tracking system. Method: In order to measure the relative importance of bugs in the issue tracking system, we propose the construction of a dependency graph based on the reported dependency-blocking information in the issue tracking system. Two metrics, namely depth and degree, are used to measure the relative importance of the bugs. However, there is uncertainty in the dependency graph structure as the dependency information is discovered manually and gradually. Owing to this uncertainty, prioritization of bugs in the descending order of depth and degree may be misleading. To handle the uncertainty, we propose a novel approach of a partially observable Markov decision process (POMDP) and partially observable Monte Carlo planning (POMCP). Result: To check the feasibility of the proposed approach, we analyzed seven years of data from an open source project, Firefox, and a commercial project. We compared the proposed policy with the developer policy, maximum policy, and random policy. Conclusion: The results suggest that software practitioners do not consider the relative importance of bugs in their current practice. The proposed framework can be combined with practitioners’ expertise to prioritize bugs more effectively and take the depth and degree of bugs into account. In practice, the POMDP framework with the POMCP planner can help practitioners sequentially select bugs to minimize the connectivity of the dependency graph.

Computing optimal (s, S) policies in inventory models with continuous demands

1985 ◽  
Vol 17 (02) ◽  
pp. 424-442 ◽  
Author(s):  
A. Federgruen ◽  
P. Zipkin
Keyword(s):  
Markov Decision Process ◽  
Decision Process ◽  
Inventory Models ◽  
Cost Criterion ◽  
Continuous State Space ◽  
Continuous State ◽  
Phase Type ◽  
Phase Type Distributions ◽  
Markov Decision ◽  
Continuous Demand

Special algorithms have been developed to compute an optimal (s, S) policy for an inventory model with discrete demand and under standard assumptions (stationary data, a well-behaved one-period cost function, full backlogging and the average cost criterion). We present here an iterative algorithm for continuous demand distributions which avoids any form of prior discretization. The method can be viewed as a modified form of policy iteration applied to a Markov decision process with continuous state space. For phase-type distributions, the calculations can be done in closed form.

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