This repository is a companion page for the paper "How Maintainability Issues of Android Apps Evolve" accepted for publication at the International conference on Software Maintenance and Evolution (ICSME 2018).

It contains all the material required to replicate our analysis, including (i) the raw input data (ii) the statistical analysis scripts, and (iii) 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.

The data used for this study can be obtained by executing the scripts available here

1. githubCrawler.py - Mines Github repository for open-source, published apps
2. fdroidCrawler.py - Mines Fdroid repository for open-source, published apps
3. Wikipedia data - Manually extracted apps that are published on Google Play and have source code on Github, available here
4. csvMerger.py - Merges the three sources into one csv
5. googlePlayPageChecker.py - Identifies the existence of Google Play Store page reported in links in repositories
6. csvDuplicatesRemover.py - Removes duplicate entries from the csv
7. androidManifestChecker.py - Filters apps that do not have a corresponding Manifest file
8. appRootAdder.py - Adds app root folder locations to apps.
9. appRootFilter.py - Filters the apps for which the root folder could not be identified.

To collect data about snapshot series execute either one of the scripts available here

1. cloneRepos.py - Snapshot series creation in Python, given a .csv file containing information about apps (with timestamps), creates a snapshot series
2. processAllSnapshots.sh - Bash script, given a .csv file containing information about apps (with timestamps), creates a snapshot series for each, and clones the required snapshots. Allows for specifying a different time-window and the addition of different static code analysis tools

Dataset .csv files containing the results of the data collection scripts are available here

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/ICSME2018ReplicationPackage) and (ii) execute the analysis scripts in the following order.

1. dataLoader.r - Load the .csv files containing the raw data
2. RQ1_analysis.r - Perform all analysis and plotting processes related to RQ1
3. RQ2_tsAnalysis.r - Build the time series, their decompositions, and plots (preliminary for the other steps)
4. RQ2_tsStationality.r - Check and store the stationarity of the time series of each app for each maintainability issue
5. RQ2_analysis.r - Perform all analysis and plotting activities related to RQ2
6. RQ3_outlierCommitFilter.r - Identify and filter the commits belonging to maintainability hotspots for each type of maintainability issue
7. RQ3_analysis.r - Perform all analysis and plotting activities related to RQ3

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

• apps.csv - Dataset containing demographic information of the Android application considered
• commits.csv - Dataset containing the entirety of the commits messages of the applications considered
• snapshots.csv - Dataset containing the evolutionary data of the application considered, such as the maintainability issue density.

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.

This reposisory is structured as follows:

ReplicationPackage2018
 .
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 |--- 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.
 |
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 |--- data/             Raw input data of the analysis processes.
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 |--- dataCollection/   Data collection scripts.
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 |--- labelledData/     Commits labelled according to the manual labelling process