The Experts below are selected from a list of 27 Experts worldwide ranked by ideXlab platform

Ashish Sureka - One of the best experts on this subject based on the ideXlab platform.

  • COMPSAC - Anvaya: An Algorithm and Case-Study on Improving the Goodness of Software Process Models Generated by Mining Event-Log Data in Issue Tracking Systems
    2016 IEEE 40th Annual Computer Software and Applications Conference (COMPSAC), 2016
    Co-Authors: Prerna Juneja, Divya Kundra, Ashish Sureka
    Abstract:

    Issue Tracking Systems (ITS) such as Bugzilla can be viewed as Process Aware Information Systems (PAIS) generating Event-logs during the life-cycle of a bug report. Process Mining consists of mining Event logs generated from PAIS for process model discovery, conformance and enhancement. We apply process map discovery techniques to mine Event Trace Data generated from ITS of open source Firefox browser project to generate and study process models. Bug life-cycle consists of diversity and variance. Therefore, the process models generated from the Event-logs are spaghetti-like with large number of edges, inter-connections and nodes. Such models are complex to analyse and difficult to comprehend by a process analyst. We improve the Goodness (fitness and structural complexity) of the process models by splitting the Event-log into homogeneous subsets by clustering structurally similar Traces. We adapt the K-Medoid clustering algorithm with two different distance metrics: Longest Common Subsequence (LCS) and Dynamic Time Warping (DTW). We evaluate the goodness of the process models generated from the clusters using complexity and fitness metrics. We study back-forth and self-loops, bug reopening, and bottleneck in the clusters obtained and show that clustering enables better analysis. We also propose an algorithm to automate the clustering process-the algorithm takes as input the Event log and returns the best cluster set.

  • Anvaya: An Algorithm and Case-Study on Improving the Goodness of Software Process Models generated by Mining Event-Log Data in Issue Tracking System
    arXiv: Software Engineering, 2015
    Co-Authors: Prerna Juneja, Divya Kundra, Ashish Sureka
    Abstract:

    Issue Tracking Systems (ITS) such as Bugzilla can be viewed as Process Aware Information Systems (PAIS) generating Event-logs during the life-cycle of a bug report. Process Mining consists of mining Event logs generated from PAIS for process model discovery, conformance and enhancement. We apply process map discovery techniques to mine Event Trace Data generated from ITS of open source Firefox browser project to generate and study process models. Bug life-cycle consists of diversity and variance. Therefore, the process models generated from the Event-logs are spaghetti-like with large number of edges, inter-connections and nodes. Such models are complex to analyse and difficult to comprehend by a process analyst. We improve the Goodness (fitness and structural complexity) of the process models by splitting the Event-log into homogeneous subsets by clustering structurally similar Traces. We adapt the K-Medoid clustering algorithm with two different distance metrics: Longest Common Subsequence (LCS) and Dynamic Time Warping (DTW). We evaluate the goodness of the process models generated from the clusters using complexity and fitness metrics. We study back-forth \& self-loops, bug reopening, and bottleneck in the clusters obtained and show that clustering enables better analysis. We also propose an algorithm to automate the clustering process -the algorithm takes as input the Event log and returns the best cluster set.

Paulo J. Santos - One of the best experts on this subject based on the ideXlab platform.

  • A user interface evaluation environment using synchronized video, visualizations and Event Trace Data
    Software Quality Journal, 1995
    Co-Authors: Albert N. Badre, Mark Guzdial, Scott E. Hudson, Paulo J. Santos
    Abstract:

    This paper presents a simple but very powerful technique to support user interface evaluation along with a prototype open environment — I-Observe , the Interface OBServation, Evaluation, Recording, and Visualization Environment. I-Observe's technique requires recording user interface sessions in multiple modalities, both as a Trace of interesting Events and through video images. It provides tools to allow the user interface evaluator either to analyze and visualize the Event Tracer Data, or to combine the Event Trace and video modalities. The analyst using I-Observe can search the Event stream for patterns of interesting or important user actions, then use the recorded timestamps associated with these actions to present only the sections of the video recording of interest. This allows the analyst to study, for example, all places where the user invokes a help system or a particular command to be observed, without manually searching the recording or sitting through long sessions of unrelated interactions. By combining the precise recording of automatic Event Trace capture with the rich contextual information that can be captured in a video and audio recording, this technique allows analyses to be performed that would not be practical with either media alone.

Prerna Juneja - One of the best experts on this subject based on the ideXlab platform.

  • COMPSAC - Anvaya: An Algorithm and Case-Study on Improving the Goodness of Software Process Models Generated by Mining Event-Log Data in Issue Tracking Systems
    2016 IEEE 40th Annual Computer Software and Applications Conference (COMPSAC), 2016
    Co-Authors: Prerna Juneja, Divya Kundra, Ashish Sureka
    Abstract:

    Issue Tracking Systems (ITS) such as Bugzilla can be viewed as Process Aware Information Systems (PAIS) generating Event-logs during the life-cycle of a bug report. Process Mining consists of mining Event logs generated from PAIS for process model discovery, conformance and enhancement. We apply process map discovery techniques to mine Event Trace Data generated from ITS of open source Firefox browser project to generate and study process models. Bug life-cycle consists of diversity and variance. Therefore, the process models generated from the Event-logs are spaghetti-like with large number of edges, inter-connections and nodes. Such models are complex to analyse and difficult to comprehend by a process analyst. We improve the Goodness (fitness and structural complexity) of the process models by splitting the Event-log into homogeneous subsets by clustering structurally similar Traces. We adapt the K-Medoid clustering algorithm with two different distance metrics: Longest Common Subsequence (LCS) and Dynamic Time Warping (DTW). We evaluate the goodness of the process models generated from the clusters using complexity and fitness metrics. We study back-forth and self-loops, bug reopening, and bottleneck in the clusters obtained and show that clustering enables better analysis. We also propose an algorithm to automate the clustering process-the algorithm takes as input the Event log and returns the best cluster set.

  • Anvaya: An Algorithm and Case-Study on Improving the Goodness of Software Process Models generated by Mining Event-Log Data in Issue Tracking System
    arXiv: Software Engineering, 2015
    Co-Authors: Prerna Juneja, Divya Kundra, Ashish Sureka
    Abstract:

    Issue Tracking Systems (ITS) such as Bugzilla can be viewed as Process Aware Information Systems (PAIS) generating Event-logs during the life-cycle of a bug report. Process Mining consists of mining Event logs generated from PAIS for process model discovery, conformance and enhancement. We apply process map discovery techniques to mine Event Trace Data generated from ITS of open source Firefox browser project to generate and study process models. Bug life-cycle consists of diversity and variance. Therefore, the process models generated from the Event-logs are spaghetti-like with large number of edges, inter-connections and nodes. Such models are complex to analyse and difficult to comprehend by a process analyst. We improve the Goodness (fitness and structural complexity) of the process models by splitting the Event-log into homogeneous subsets by clustering structurally similar Traces. We adapt the K-Medoid clustering algorithm with two different distance metrics: Longest Common Subsequence (LCS) and Dynamic Time Warping (DTW). We evaluate the goodness of the process models generated from the clusters using complexity and fitness metrics. We study back-forth \& self-loops, bug reopening, and bottleneck in the clusters obtained and show that clustering enables better analysis. We also propose an algorithm to automate the clustering process -the algorithm takes as input the Event log and returns the best cluster set.

Albert N. Badre - One of the best experts on this subject based on the ideXlab platform.

  • A user interface evaluation environment using synchronized video, visualizations and Event Trace Data
    Software Quality Journal, 1995
    Co-Authors: Albert N. Badre, Mark Guzdial, Scott E. Hudson, Paulo J. Santos
    Abstract:

    This paper presents a simple but very powerful technique to support user interface evaluation along with a prototype open environment — I-Observe , the Interface OBServation, Evaluation, Recording, and Visualization Environment. I-Observe's technique requires recording user interface sessions in multiple modalities, both as a Trace of interesting Events and through video images. It provides tools to allow the user interface evaluator either to analyze and visualize the Event Tracer Data, or to combine the Event Trace and video modalities. The analyst using I-Observe can search the Event stream for patterns of interesting or important user actions, then use the recorded timestamps associated with these actions to present only the sections of the video recording of interest. This allows the analyst to study, for example, all places where the user invokes a help system or a particular command to be observed, without manually searching the recording or sitting through long sessions of unrelated interactions. By combining the precise recording of automatic Event Trace capture with the rich contextual information that can be captured in a video and audio recording, this technique allows analyses to be performed that would not be practical with either media alone.

Divya Kundra - One of the best experts on this subject based on the ideXlab platform.

  • COMPSAC - Anvaya: An Algorithm and Case-Study on Improving the Goodness of Software Process Models Generated by Mining Event-Log Data in Issue Tracking Systems
    2016 IEEE 40th Annual Computer Software and Applications Conference (COMPSAC), 2016
    Co-Authors: Prerna Juneja, Divya Kundra, Ashish Sureka
    Abstract:

    Issue Tracking Systems (ITS) such as Bugzilla can be viewed as Process Aware Information Systems (PAIS) generating Event-logs during the life-cycle of a bug report. Process Mining consists of mining Event logs generated from PAIS for process model discovery, conformance and enhancement. We apply process map discovery techniques to mine Event Trace Data generated from ITS of open source Firefox browser project to generate and study process models. Bug life-cycle consists of diversity and variance. Therefore, the process models generated from the Event-logs are spaghetti-like with large number of edges, inter-connections and nodes. Such models are complex to analyse and difficult to comprehend by a process analyst. We improve the Goodness (fitness and structural complexity) of the process models by splitting the Event-log into homogeneous subsets by clustering structurally similar Traces. We adapt the K-Medoid clustering algorithm with two different distance metrics: Longest Common Subsequence (LCS) and Dynamic Time Warping (DTW). We evaluate the goodness of the process models generated from the clusters using complexity and fitness metrics. We study back-forth and self-loops, bug reopening, and bottleneck in the clusters obtained and show that clustering enables better analysis. We also propose an algorithm to automate the clustering process-the algorithm takes as input the Event log and returns the best cluster set.

  • Anvaya: An Algorithm and Case-Study on Improving the Goodness of Software Process Models generated by Mining Event-Log Data in Issue Tracking System
    arXiv: Software Engineering, 2015
    Co-Authors: Prerna Juneja, Divya Kundra, Ashish Sureka
    Abstract:

    Issue Tracking Systems (ITS) such as Bugzilla can be viewed as Process Aware Information Systems (PAIS) generating Event-logs during the life-cycle of a bug report. Process Mining consists of mining Event logs generated from PAIS for process model discovery, conformance and enhancement. We apply process map discovery techniques to mine Event Trace Data generated from ITS of open source Firefox browser project to generate and study process models. Bug life-cycle consists of diversity and variance. Therefore, the process models generated from the Event-logs are spaghetti-like with large number of edges, inter-connections and nodes. Such models are complex to analyse and difficult to comprehend by a process analyst. We improve the Goodness (fitness and structural complexity) of the process models by splitting the Event-log into homogeneous subsets by clustering structurally similar Traces. We adapt the K-Medoid clustering algorithm with two different distance metrics: Longest Common Subsequence (LCS) and Dynamic Time Warping (DTW). We evaluate the goodness of the process models generated from the clusters using complexity and fitness metrics. We study back-forth \& self-loops, bug reopening, and bottleneck in the clusters obtained and show that clustering enables better analysis. We also propose an algorithm to automate the clustering process -the algorithm takes as input the Event log and returns the best cluster set.