Mining Branching LSCs
URL
Change Log
| When | What |
|---|---|
| February 5, 2016 | Fixed BibTeX, abstract, and paper link |
| January 29, 2016 | Donated by Shahar Maoz |
Reference
Studies who have been using the data (in any form) are required to include the following reference:
@INPROCEEDINGS{6693102,
author={Fahland, D. and Lo, D. and Maoz, S.},
booktitle={Automated Software Engineering (ASE), 2013 IEEE/ACM 28th International Conference on},
title={Mining branching-time scenarios},
year={2013},
pages={443-453},
keywords={data mining;formal verification;program testing;statistical analysis;behavior models extraction;branching-time scenarios mining;conditional live sequence charts;statistical data-mining algorithm;Abstracts;Context;Data mining;Educational institutions;Semantics;Testing;Weight measurement},
doi={10.1109/ASE.2013.6693102},
month={Nov},}
About the Data
Overview of Data
Two sets of execution traces from two object-oriented applications: CrossFTP (an FTP client), Columba (an email client). An event in an execution trace denotes one method call from on object in the application (to itself or to another object). Each trace contains events of one execution of the application (from launch to termination). In addition, the dataset contains results of an experiment for mining linear-time and branching-time scenarios in the form of Live Sequence Charts (LSCs) using different parameters. An index file describes the data and the experiments.
Paper Abstract
Specification mining extracts candidate specification from existing systems, to be used for downstream tasks such as testing and verification. Specifically, we are interested in the extraction of behavior models from execution traces. In this paper we introduce mining of branching-time scenarios in the form of existential, conditional Live Sequence Charts, using a statistical data-mining algorithm. We show the power of branching scenarios to reveal alternative scenario-based behaviors, which could not be mined by previous approaches. The work contrasts and complements previous works on mining linear-time scenarios. An implementation and evaluation over execution trace sets recorded from several real-world applications shows the unique contribution of mining branching-time scenarios to the state-of-the-art in specification mining.