changes for jar
This commit is contained in:
fixminer
@@ -24,7 +24,7 @@ Fixminer is a systematic and automated approach to mine relevant and actionable
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## II. Environment setup
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* OS: macOS Mojave (10.14.3)
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* JDK8: Oracle jdk1.8 (**important!**)
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* JDK8: (**important!**)
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* Download and configure Anaconda
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* Create an python environment using the [environment file](environment.yml)
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```powershell
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@@ -32,7 +32,12 @@ Fixminer is a systematic and automated approach to mine relevant and actionable
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```
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* After creating the environment, activate it. It is containing necessary dependencies for redis, and python.
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```powershell
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source activate redisEnv
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source activate fixminerEnv
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```
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* Update the config.yml file with the corresponding paths in your computer. An example config.yml file could be found under
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```powershell
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fixminer_source/src/main/resources/config.yml
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```
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<!---
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[fixminer.sh](python/fixminer.sh)
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@@ -45,95 +50,52 @@ In order to launch FixMiner, execute [fixminer.sh](python/fixminer.sh)
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bash fixminer.sh /Users/..../enhancedASTDiff/python/ stats
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--->
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## III. Replication Data
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Replication Data:
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[singleBR.pickle](python/data/singleBR.pickle)
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This pickle contains the list bug reports (i.e. bid) with the their corresponding fixes (i.e. commit) for each project in the dataset (i.e. project).
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[bugReports.7z.00X](python/data/bugReports.7z.001)
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This is the dump of the bug reports archive extracted from each commit. These bug reports are not necessarily considered as BUG,CLOSED; this archive is the contins initial bug reports before identifying the fixes.
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[gumInput.7z.001](python/data/gumInput.7z.001)
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This archive contains all the patches in our dataset, formatted in a way that can be processed by GumTree (i.e DiffEntries, prevFiles, revFiles)
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[ALLbugReportsComplete.pickle](python/data/ALLbugReportsComplete.pickle)
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The pickle object that represents the bug reports under the following columns 'bugReport', 'summary', 'description', 'created', 'updated', 'resolved', 'reporterDN', 'reporterEmail','hasAttachment', 'attachmentTime', 'hasPR', 'commentsCount'
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#### Data Viewer
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The data provided with replication package is listed in directory [python/data](python/data)
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The data is stored in different formats. (e.g. pickle, db, csv, etc..)
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The see content of the .pickle file the following script could be used.
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```python
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import pickle as p
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import gzip
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def load_zipped_pickle(filename):
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with gzip.open(filename, 'rb') as f:
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loaded_object = p.load(f)
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return loaded_object
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```
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Usage
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```python
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result = load_zipped_pickle('code/LANGbugReportsComplete.pickle')
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# Result is pandas object which can be exported to several formats
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# Details on how to export is listed in offical library documentation
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# https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html
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```
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## IV. Step-by-Step execution
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#### Before running
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* Update [config file](python/config.yml) with corresponding user paths.
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* Update [config file](src/main/resources/config.yml) with corresponding user paths.
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* Active the conda environment from shell
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```powershell
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source activate redisEnv
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source activate fixminerEnv
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```
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In order to launch FixMiner, execute [fixminer.sh](python/fixminer.sh)
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bash fixminer.sh [PATH_TO_PYTHON_FOLDER] [OPTIONS]
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e.g. bash fixminer.sh Users/fixminer-core/python/ stats
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#### Running Options
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bash fixminer.sh [JOB] [CONFIG_FILE]
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e.g. bash fixminer.sh dataset4c /Users/projects/release/fixminer_source/src/main/resources/config.yml
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*FixMiner* needs to specify an option to run.
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#### Job Types
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1. 'dataset': Create a mining dataset from the project speficied in [subjects.csv](python/data/subjects.csv)
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Eventually dataset option is the execution of the following steps, which are merged under 'dataset' option
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for this demo. Eventually single options can be activated by commenting out the corresponding option in [main.py](python/main.py)
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*FixMiner* needs to specify a job to run.
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`clone` : Clone target project repository.
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`collect` : Collect all commit from repository.
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`fix` : Collect commits linked to a bug report.
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`bugPoints` : Identify the snapshot of the repository before the bug fixing commit introducted.
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`brDownload` : Download bug reports recovered from commit log
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`brParser` : Parse bug reports to select the bug report where type labelled as BUG and status as RESOLVED or CLOSED
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1. __dataset4j__ / __dataset4c__: Create a java/c mining dataset from the projects listed in [subjects.csv](python/data/subjects.csv) or [datasets.csv](python/data/datasets.csv) for c
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2. 'richEditScript': Rich edit script computation step.
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2. __richEditScript__: Calls the jar file produced as the results as maven package to compute Rich edit scripts.
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This step can be invoke natively from java or using the [Launcher](src/main/java/edu/lu/uni/serval/richedit/Launcher.java) with appropriate arguments.
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```powershell
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java -jar FixPatternMiner-1.0.0-jar-with-dependencies.jar /Users/projects/release/fixminer_source/src/main/resources/config.yml RICHEDITSCRIPT
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```
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3. 'shapeSI': Search index creation for shapes. The output of this step is written to [pairs](python/data/pairs)
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3. __shapeSI__: Search index creation for shapes. The output of this step is written to __pairs__ which will be generated under __datapath__ in [config file](src/main/resources/config.yml)
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4. 'compareShapes' : ShapeTree comparison
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4. __compareShapes__ : Calls the jar file produced as the results as maven package to compare the trees.
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This step can be invoke natively from java or using the [Launcher](src/main/java/edu/lu/uni/serval/richedit/Launcher.java) with appropriate arguments.
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```powershell
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java -jar FixPatternMiner-1.0.0-jar-with-dependencies.jar /Users/projects/release/fixminer_source/src/main/resources/config.yml COMPARETREES
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```
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5. 'cluster': Forms clusters of identical shapetree. The output of this step is written to [shapes](python/data/shapes)
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5. 'cluster': Forms clusters of identical shapetree. The output of this step is written to [shapes](python/data/shapes)
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6. 'actionSI': Search index creation for actions. The output of this step is written to [pairs](python/data/pairsAction)
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@@ -233,6 +195,51 @@ It is necessary to run the FixMiner, following the order.
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-->
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## III. Replication Data
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Replication Data:
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[singleBR.pickle](python/data/singleBR.pickle)
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This pickle contains the list bug reports (i.e. bid) with the their corresponding fixes (i.e. commit) for each project in the dataset (i.e. project).
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[bugReports.7z.00X](python/data/bugReports.7z.001)
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This is the dump of the bug reports archive extracted from each commit. These bug reports are not necessarily considered as BUG,CLOSED; this archive is the contins initial bug reports before identifying the fixes.
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[gumInput.7z.001](python/data/gumInput.7z.001)
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This archive contains all the patches in our dataset, formatted in a way that can be processed by GumTree (i.e DiffEntries, prevFiles, revFiles)
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[ALLbugReportsComplete.pickle](python/data/ALLbugReportsComplete.pickle)
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The pickle object that represents the bug reports under the following columns 'bugReport', 'summary', 'description', 'created', 'updated', 'resolved', 'reporterDN', 'reporterEmail','hasAttachment', 'attachmentTime', 'hasPR', 'commentsCount'
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#### Data Viewer
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The data provided with replication package is listed in directory [python/data](python/data)
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The data is stored in different formats. (e.g. pickle, db, csv, etc..)
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The see content of the .pickle file the following script could be used.
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```python
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import pickle as p
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import gzip
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def load_zipped_pickle(filename):
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with gzip.open(filename, 'rb') as f:
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loaded_object = p.load(f)
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return loaded_object
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```
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Usage
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```python
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result = load_zipped_pickle('code/LANGbugReportsComplete.pickle')
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# Result is pandas object which can be exported to several formats
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# Details on how to export is listed in offical library documentation
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# https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.html
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```
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