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@@ -10,7 +10,8 @@ This repository responds to the call for public SZZ implementations by Rodrígue
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2. [Running SZZ Unleashed](#szz_usage)
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3. [SZZ Unleashed with Docker](#szz_docker)
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4. [Example Application: Training a Classifier for Just-in-Time Bug Prediction](#feat_extract)
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-5. [Authors](#authors)
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+5. [Examples and executables](#examples_n_exec)
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+6. [Authors](#authors)
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## Background <a name="background"></a>
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@@ -36,6 +37,8 @@ python fetch.py --issue-code <issue_code> --jira-project <jira_project_base_url>
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```
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passing as parameters the code used for the project issues on JIRA and the name of the JIRA repository of the project (e.g., _issues.jenkins-ci.org_). The script creates a directory with issues (see issues folder in the [figure](#workflow)). These issues will later on be used by the `find_bug_fixes.py` script.
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+A more thorough example of this script can be found [here](./examples/Fetch.md).
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+
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### Step 2. Preprocess the git log output (SZZ pre-step) ###
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Second we need to convert the `git log` output to something that can be processed. That requires a local copy of the repository that we aim to analyze, [Jenkins Core Repository](https://github.com/jenkinsci/jenkins). Onced cloned, one can now run the **git_log_to_array.py** script (see 2 in the [figure](#workflow)). The script requires an absolute path to the cloned repository and a SHA-1 for an initial commit.
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```python
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@@ -43,6 +46,8 @@ python git_log_to_array.py --from-commit <SHA-1_of_initial_commit> --repo-path <
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```
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Once executed, this creates a file `gitlog.json` that can be used together with issues that we created with `fetch.py` script.
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+An example of this script and what it produces can be found [in the examples](./examples/GitlogToArray.md).
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+
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### Step 3. Identify bug-fixing commits (SZZ Phase 1) ###
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Now using the `find_bug_fixes.py` (see 3 in the [figure](#workflow)) and this file, we can get a json file
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that contains the Issue and its corresponding commit SHA-1, the commit date, the creation date and the resolution date. Just run:
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@@ -51,6 +56,8 @@ python find_bug_fixes.py --gitlog <path_to_gitlog_file> --issue-list <path_to_is
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```
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The output is `issue_list.json` which is later used in the SZZ algorithm.
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+An example output of this script can be found in [the examples](./examples/FindBugFixes.md).
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+
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### Identify bug-introducing commits (SZZ Phase 2) ###
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This implementation works regardless which language and file type. It uses
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[JGIT](https://www.eclipse.org/jgit/) to parse a git repository.
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@@ -97,6 +104,10 @@ way and it includes duplicates when it comes to both introducers and fixes. A
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fix can be made several times and a introducer could be responsible for many
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fixes.
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+## Configuring SZZ Unleashed
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+
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+A description of how to configure SZZUnleashed further can be found in [the examples](./examples/BugIntroducersFinder.md).
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+
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## Use SZZ Unleashed with Docker <a name="szz_docker"></a>
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A more thorough instruction in using docker to produce the results can be found in [doc/Docker.md](doc/Docker.md). Below is a very brief instruction.
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@@ -223,6 +234,10 @@ be made. In this example, we train a random forest classifier. To do this, run t
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python model.py train
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```
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+## Examples and executables <a name="examples_n_exec"></a>
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+
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+In [the examples](./examples) directory, one can find documents containing descriptions about each script. There is also [a data directory](./examples/data) where one can find data produced by the scripts. It can be used to either study how the output should look like or if anyone just want a dataset to train on.
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+
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## Authors <a name="authors"></a>
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[Oscar Svensson](mailto:wgcp92@gmail.com)
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ossv