S2-group / AndroidPerformanceIssues

Replication package of the scientific paper titled "Characterizing the evolution of statically-detectable performance issues of Android apps" submitted to the Empirical Software Engineering journal

AndroidPerformanceIssues

Replication package of the scientific paper titled "Characterizing the evolution of statically-detectable performance issues of Android apps" published to the Empirical Software Engineering journal

It contains all the material required to replicate our analysis, including (i) the raw input data (ii) the data extraction and context selection scripts (iii) the statistical analysis scripts, and (iv) the analysis results in form of data, plots, etc. Some additional analyses and results, not included in the paper due to space limitations, are also provided.

How to cite this study

If this study is helping your research, consider to cite it is as follows, thanks!

@article{EMSE_2020,
  url = { http://www.ivanomalavolta.com/files/papers/EMSE_2020.pdf },
  publisher = { Springer },
  year = { 2020 },
  pages = { to appear },
  number = {  },
  volume = { Preprint },
  journal = { Empirical Software Engineering },
  author = { Teerath Das and Massimiliano {Di Penta} and Ivano Malavolta },
  title = { Characterizing the evolution of statically-detectable performance issues of Android apps },
}

Analysis replication

The totality of the statistical analysis scripts utilized for the study are available here In order to replicate the analysis of the study (i) clone the repository (git clone https://github.com/S2-group/AndroidPerformanceIssues) and (ii) execute the analysis scripts in the following order.

1. RQ0_analysis.R - Perform all analysis and plotting processes related to RQ0
2. RQ1_analysis.R - Perform all analysis and plotting processes related to RQ1
3. RQ2_analysis.R - Perform all analysis and plotting processes related to RQ2
4. RQ3_analysis.R - Perform all analysis and plotting processes related to RQ3
5. RQ4_analysis.R - Perform all analysis and plotting processes related to RQ4

Raw input Data

The raw input data utilized for the statistical analysis is available here Specifically, the analyzed dataset is composed of the following files:

• targetedApps.csv - Dataset containing demographic information of the Android applications considered in our study.
• PCount.csv - Dataset containing the entirety of the commits of the applications considered and their the count of performance issues observed.
• identifiedPerformanceIssues.csv - Dataset containing the evolutionary data of the application considered, such as the Performance issues.

Results and plots

The results produced in order to answer our research question are provided here. The totality of the plots generated during the analysis processes are instead provided here. This includes also diagrams which, due to space limitations, were not included in the paper.

Directory Structure Overview

This reposisory is structured as follows:

AndroidPerformanceIssues
 .
 |
 |--- analysis/         Input of the algorithms, i.e. fault matrix, coverage information, and BB representation of subjects.
 |      |
 |      |
 |      |--- plots/     Plots generated for the analysis processes. 
 |      |
 |      |--- results/   Raw output data generated from the analysis.
 |
 |
 |--- data/             Raw input data of the analysis processes.
 |
 |--- labelledData/     Evolution categories labelled according to the manual labelling process and Cohen Kappa Values for the level greement between these categories.      |
 |--- selection&extraction/     All Python and Unix scripts for context selection and data extraction.
        |
        |
        |--- appSelectionScripts/     Python and Unix Shell scripts for targeted app selection processes. 
        |
        |--- dataExtractionScripts/   Python and Unix Shell scripts for data extraction processes.                          of a