fixminer
255 lines
9.8 KiB
Markdown
255 lines
9.8 KiB
Markdown
# FixMiner [](https://coveralls.io/github/SerVal-DTF/fixminer_source?branch=master)
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# Code of FixMiner
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Reference: [FixMiner: Mining Relevant Fix Patterns for Automated Program Repair](http://arxiv.org/pdf/1810.01791) (Empirical Software Engineering, [doi:10.1007/s10664-019-09780-z](https://doi.org/10.1007/s10664-019-09780-z))
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## Citing FixMiner
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You can cite FixMiner using the following bibtex:
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```
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@article{koyuncu2020fixminer,
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title={Fixminer: Mining relevant fix patterns for automated program repair},
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author={Koyuncu, Anil and Liu, Kui and Bissyand{\'e}, Tegawend{\'e} F and Kim, Dongsun and Klein, Jacques and Monperrus, Martin and Le Traon, Yves},
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journal={Empirical Software Engineering},
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pages={1--45},
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year={2020},
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publisher={Springer}
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}
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```
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# FixMiner
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* [I. Introduction of FixMiner](#user-content-i-introduction)
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* [II. Environment setup](#user-content-ii-environment)
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* [III. Replication Data](#user-content-iii-data)
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* [IV. Step-by-Step execution](#user-content-iv-how-to-run)
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<!--
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* [V. Evaluation Result](#user-content-v-evaluation-result)
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* [VI. Generated Patches](#user-content-vi-generated-patches)
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* [VII. Structure of the project](#user-content-vii-structure-of-the-project)
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-->
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## I. Introduction
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Fixminer is a systematic and automated approach to mine relevant and actionable fix patterns for automated program repair.
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## II. Environment setup
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* OS: macOS Mojave (10.14.3)
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* JDK8: (**important!**)
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* To mine from c code, [srcml 1.0.0](https://www.srcml.org/#download)
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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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conda env create -f environment.yml
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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 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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Unzip it,to the datasetPath path indicated in app.properties.
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7z x allDataset.7z
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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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## IV. Step-by-Step execution
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#### Before running
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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 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 [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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#### Job Types
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*FixMiner* needs to specify a job to run.
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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__: 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__ folder which will be generated under __datapath__ in [config file](src/main/resources/config.yml)
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4. __compare__ : 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 COMPARE
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```
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5. __cluster__ : Forms clusters of identical trees. The output of this step is written to __shapes__ folder which will be generated under __datapath__ in [config file](src/main/resources/config.yml)
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<!--
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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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7. 'compareActions' : ActionTree comparison
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8. 'clusterActions': Forms clusters of identical ActionTree. The output of this step is written to [shapes](python/data/actions)
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9. 'tokenSI': Search index creation for shapes. The output of this step is written to [pairs](python/data/pairsToken)
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10. 'compareTokens' : TokenTree comparison
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11. 'clusterTokens': Forms clusters of identical TokenTree. The output of this step is written to [shapes](python/data/tokens)
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12. 'stats' : Calculate some statistics about patterns under python/data/statsactions.csv,statsshapes.csv,statstokens.csv, and export FixPatterns of APR integration [fixpatterns](actionPattern2verify.csv)
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-->
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<!--
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App.properties:
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FixMiner consists of several jobs that needs to run in order to extract fix pattern from the dataset.
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It is necessary to run the FixMiner, following the order.
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1.ENHANCED AST DIFF calcuation
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By setting the jobType = ENHANCEDASTDIFF. This will create the ENHANCEDASTDIFF for the dataset regardless of the actionType.
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2.CACHE the enhanced AST Diff into memory cache
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By setting the jobType = CACHE
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3.SI search index construction.
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By setting the jobType = SI
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4.SIMI in order to compare the similarity between the trees.
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By setting the jobType = SIMI
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5.LEVEL1 mining
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By setting the jobType = LEVEL1
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6.LEVEL2 mining
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By setting the jobType = LEVEL2
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7.LEVEL3 mining
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By setting the jobType = LEVEL3
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A mining is iteration is executed for the actionType. In order to execute for all the actionTypes, the iteration should be repeated from 2-7 by changing the actionType.
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There are some additional parameters in the app.config.
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actionType
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The admitted values are UPD,INS,DEL,MOV,MIX, which represents the ENHANCEDASTDIFF actions.
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UPD/INS/DEL/MOV considers tree where a single action operation is done in the action set
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MIX considers any action.
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parallelism
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The engine to use for parallelism. It is either FORKJOIN or AKKA.
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FORKJOIN is recommended is the FixMiner is running on a single machine.
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AKKA is suggested for distributed machines.
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numOfWorkers
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The number of workers that will be generated when AKKA is selected as the parallelism engine.
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cursor
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The maximum number of pairs in during the search index SI creation.
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eDiffTimeout
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The timeout value in seconds for the Enhanced Diff computation (ENHANCEDASTDIFF).
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In case ENHANCEDASTDIFF step logs timeouts, this value can be increase.
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The following parameters should be used when dealing with extremely large dataset. Otherwise, default values are suggested.
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isBigPair
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This flag when set to true, splits the pairs that into chunks as ..0.txt,1.txt etc.
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chunk
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The extension of the pairs files. When isBigPair is set to false(which is default), it needs to be set as .csv
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When isBigPair mode is activated then the SIMI step executed for each chunk by stepping the chunk as 0.txt, 1,txt)
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## V. Evaluation Result
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## VI. Generated Patches
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## VII. Structure of the project
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-->
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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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-->
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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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