DOI: 10.5281/zenodo.15195200
title: User Interface Logs based on mockups or real-life screenshots for task mining applications

description: This dataset corresponds to the problems analyzed in the work "Assessing Reproducibility in Screenshot-Based Task Mining: A Decision Discovery Perspective," published in the Information Systems Journal.

The artifacts provided correspond to multiple instances of the execution of a particular process model, covering all its variants in the form of UI Logs. These UI Logs are divided into two groups:



A set where the process is executed in a synthetic environment, using mockups to represent the user interface.

A set where real screenshots of the user interfaces involved in the process are used.


Additionally, UI Logs are synthetically generated from an original UI Log in both cases.

For generating the UI Logs, a real-world process based on handling unsubscription requests from users of a telephone company has been selected. This case was selected based on the following criteria (1) the process is replicated from a real company, (2) decision discovery relies on visual elements present in the screenshots, specifically an email attachment and a checkbox in a web form. Thus, the selected process consists of 10 activities, a single decision point, and 4 process variants.

The dataset includes:



UI Logs Folder:



Contains UI logs used to evaluate the approach. The dataset contains 10 folders—5  corresponding to logs based on real screenshots and 5 to those using mockups. Inside these folders, the dataset is further structured into subfolders based on problem characteristics, with names formatted as: ProblemType_LogSize_Balanced, where LogSize is one of {75, 100, 300, 500} and Balanced is either Balanced or Imbalanced.  Therefore, each problem subfolder contains the corresponding UI Log and associated screenshots. organized into subfolders based on different problem characteristics. Each subfolder includes:



log.csv: A CSV file containing the UI log data.

Screenshots and associated metadata:



1_img.png: A sample screenshot image.

1_img.png.json: JSON file containing metadata for the corresponding screenshot.



Original obtained evaluation results:



flattened_dataset.csv: A flattened version of the dataset used for decision tree analysis.

preprocessed_df.csv: Preprocessed data frame used for analysis.

decision_tree.log: Log file documenting the decision tree process.

CHAID-tree-feature-importance.csv: CSV file detailing feature importance from the CHAID decision tree.







Process Discovery Files Folder:



Contains the necessary data about the process for the framework to parse the UI logs and identify the decision points within the process. These files include:



bpmn.bpmn: BPMN file representing the process model.

bpmn.dot: DOT file representing the BPMN process model.

pn.dot: DOT file representing the Petri net process model.

traceability.json: JSON file mapping decision point branches to rules from decision model.



These files map to the files outputted by the third phase of the proposed framework and are mocked for the purpose of restricting the evaluation to the fourth phase of the framework.



Scripts Folder:



Contains the necessary scripts for processing the UI logs to a format that can be processed by the framework, creating the experiments, populating the database of the framework with the experiment data, running the experiments, and collecting results. The scripts include:



collect_results.py: Script to collect experiment results.

db_populate.json: Configuration file for populating the database.

hierarchy_constructor.py: Script to construct the hierarchy of UI elements.

models_populate.json: Configuration file for populating models.

process_logs.py: Script to process UI logs.

process_reproducibility_data.py: Script to process reproducibility data.

process_uielements.py: Script to process UI elements.

run_experiments.py: Script to run experiments.

run_experiments.sh: Shell script to execute the experiments.






To create the evaluation objects, we generated event logs of different sizes (|L|) by deriving events from the sample event log. We consider log sizes of {75, 100, 300, 500} events. Each log contains complete process instances, ensuring that if an additional instance exceeds |L|, it is removed.

To average results across different problem instances, we trained decision trees 30 times on synthetic variations of the dataset, obtaining the mean of the metrics as experiment metadata.