Profiling Event Logs to Configure Risk Indicators for Process Delays

Pika, Anastasiia; Aalst, Wil M. P. van der; Fidge, Colin; Hofstede, Arthur H. M. ter; Wynn, Moe Thandar

doi:10.1007/978-3-642-38709-8_30

Cited by 20 publications

(14 citation statements)

References 17 publications

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“…For time series comparison we use R's wilcox method 11 that implements the Mann-Whitney U-test [7] and for trend fitting we use R's lm method 12 . The results of the analysis are visualised using the JFreeChart library 13 . Fig.…”

Section: Methodsmentioning

confidence: 99%

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An Extensible Framework for Analysing Resource Behaviour Using Event Logs

Pika

Wynn

Fidge

et al. 2014

Advanced Information Systems Engineering

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Abstract. Business processes depend on human resources and managers must regularly evaluate the performance of their employees based on a number of measures, some of which are subjective in nature. As modern organisations use information systems to automate their business processes and record information about processes' executions in event logs, it now becomes possible to get objective information about resource behaviours by analysing data recorded in event logs. We present an extensible framework for extracting knowledge from event logs about the behaviour of a human resource and for analysing the dynamics of this behaviour over time. The framework is fully automated and implements a predefined set of behavioural indicators for human resources. It also provides a means for organisations to define their own behavioural indicators, using the conventional Structured Query Language, and a means to analyse the dynamics of these indicators. The framework's applicability is demonstrated using an event log from a German bank.

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Section: Methodsmentioning

confidence: 99%

“…In our own earlier work, we showed how process risk indicators can be used to detect the risk of case delays [12,13]. Some of these previous risk indicators were related to the behaviour of resources.…”

Section: Related and Previous Workmentioning

confidence: 99%

An Extensible Framework for Analysing Resource Behaviour Using Event Logs

Pika

Wynn

Fidge

et al. 2014

Advanced Information Systems Engineering

Self Cite

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“…waiting for the assigned developer(s) to take actions. Abnormal waiting time is an indication of an issue being delayed due to lack of team cooperation or nobody wanting to deal with the issue [12]. The waiting time of an issue is from when an assignee has been assigned to the issue, until they take an action to resolve it such as changing the issue's status or submitting a pull request.…”

Section: B Potential Risk Factors For Software Issuesmentioning

confidence: 99%

“…A lack of resource planning has implications to project failures [21], and developer workload may have significant impact on the progress of a project [12,22]. A developer's workload is determined by the number of opened issues that have been assigned to the developer at a time.…”

Section: ) Changing Of Descriptionmentioning

confidence: 99%

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Characterization and Prediction of Issue-Related Risks in Software Projects

Choetkiertikul

Dam

Tran

et al. 2015

2015 IEEE/ACM 12th Working Conference on Mining Software Repositories

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Abstract-Identifying risks relevant to a software project and planning measures to deal with them are critical to the success of the project. Current practices in risk assessment mostly rely on high-level, generic guidance or the subjective judgements of experts. In this paper, we propose a novel approach to risk assessment using historical data associated with a software project. Specifically, our approach identifies patterns of past events that caused project delays, and uses this knowledge to identify risks in the current state of the project. A set of risk factors characterizing "risky" software tasks (in the form of issues) were extracted from five open source projects: Apache, Duraspace, JBoss, Moodle, and Spring. In addition, we performed feature selection using a sparse logistic regression model to select risk factors with good discriminative power. Based on these risk factors, we built predictive models to predict if an issue will cause a project delay. Our predictive models are able to predict both the risk impact (i.e. the extend of the delay) and the likelihood of a risk occurring. The evaluation results demonstrate the effectiveness of our predictive models, achieving on average 48%-81% precision, 23%-90% recall, 29%-71% F-measure, and 70%-92% Area Under the ROC Curve. Our predictive models also have low error rates: 0.39-0.75 for Macroaveraged Mean Cost-Error and and 0.7-1.2 for Macro-averaged Mean Absolute Error.

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