merge esecfse22 into develop

Author: pmbittner

INSTALL.md

@@ -66,6 +66,9 @@ To execute the replication you can run the `execute` script corresponding to you
 > #### Linux/Mac (bash):
 > `./stop-execution.sh`
 
+You might see warnings or errors reported from SLF4J like `Failed to load class "org.slf4j.impl.StaticLoggerBinder"` which you can safely ignore.
+Further troubleshooting advice can be found at the bottom of this file.
+
 The results of the verification will be stored in the [results](results) directory.
 
 ### Expected Output of the Verification
@@ -149,3 +152,8 @@ The input file must have the same format as the other dataset files (i.e., repos
 
 `Fix:` If there is a _results_ directory, delete it with elevated permission (e.g., `sudo rm -r results`). 
 Then, create a new _results_ directory without elevated permissions, or execute `git restore .` to restore the deleted directory.
+
+### Failed to load class "org.slf4j.impl.StaticLoggerBinder"
+`Problem:` An operation within the initialization phase of the logger library we use (tinylog) failed.
+
+`Fix:` Please ignore this warning. Tinylog will fall back onto a default implementation (`Defaulting to no-operation (NOP) logger implementation`) and logging will work as expected.

README.md

@@ -1,4 +1,4 @@
-# Classifying Edits to Variability in Source Code
+<img padding="10" align="right" src="https://www.acm.org/binaries/content/gallery/acm/publications/artifact-review-v1_1-badges/artifacts_evaluated_reusable_v1_1.png" alt="ACM Artifacts Evaluated Reusable" width="114" height="113"/>
 
 ![Maven](https://github.com/VariantSync/DiffDetective/actions/workflows/maven.yml/badge.svg)
 [![Documentation](https://img.shields.io/badge/Documentation-Read-purple)][documentation]
@@ -8,6 +8,8 @@
 [![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.7110095.svg)](https://doi.org/10.5281/zenodo.7110095)
 [![Status](https://img.shields.io/badge/ESEC%2FFSE'22-Badge%20Application-blue)](STATUS.md)
 
+# Classifying Edits to Variability in Source Code
+
 This is the replication package for our paper _Classifying Edits to Variability in Source Code_ accepted at the 30th ACM Joint European Software Engineering Conference and Symposium on the Foundations of Software Engineering (ESEC/FSE 2022).
 
 This replication package consists of four parts:
@@ -18,7 +20,7 @@ This replication package consists of four parts:
 4. **Dataset Overview**: We provide an overview of the 44 inspected datasets with updated links to their repositories in the file [docs/datasets.md][dataset].
 
 ## 1. DiffDetective
-DiffDetective is a java library and command-line tool to parse and classify edits to variability in git histories of preprocessor-based software product lines by creating [variation tree diffs][difftree_class] and operating on them.
+DiffDetective is a java library and command-line tool to parse and classify edits to variability in git histories of preprocessor-based software product lines by creating [variation diffs][difftree_class] and operating on them.
 
 We offer a [Docker](https://www.docker.com/) setup to easily __replicate__ the validation performed in our paper. 
 In the following, we provide a quickstart guide for running the replication.
@@ -53,45 +55,47 @@ To execute the replication you can run the `execute` script corresponding to you
 > #### Linux/Mac (bash):
 > `./stop-execution.sh`
 
+You might see warnings or errors reported from SLF4J like `Failed to load class "org.slf4j.impl.StaticLoggerBinder"` which you can safely ignore.
+Further troubleshooting advice can be found at the bottom of the [Install](INSTALL.md) file.
 
 ### 1.3 View the results in the [results][resultsdir] directory
 All raw results are stored in the [results][resultsdir] directory.
 The aggregated results can be found in the following files.
 (Note that the links below only have a target _after_ running the replication or verification.)
 - [speed statistics][resultsdir_speed_statistics]: contains information about the total runtime, median runtime, mean runtime, and more.
-- [classification results][resultsdir_classification_results]: contains information about how often each pattern was found, and more.
+- [classification results][resultsdir_classification_results]: contains information about how often each class was found, and more.
 
 Moreover, the results comprise the (LaTeX) tables that are part of our paper and appendix.
 
 ### Documentation
 
 DiffDetective is documented with javadoc. The documentation can be accessed on this [website][documentation]. Notable classes of our library are:
-- [DiffTree](https://variantsync.github.io/DiffDetective/docs/javadoc/org/variantsync/diffdetective/diff/difftree/DiffTree.html) and [DiffNode](https://variantsync.github.io/DiffDetective/docs/javadoc/org/variantsync/diffdetective/diff/difftree/DiffNode.html) implement variation tree diffs from our paper. A variation tree diff is represented by an instance of the `DiffTree` class. It stores the root node of the diff and offers various methods to parse, traverse, and analyze variation tree diffs. `DiffNode`s represent individual nodes within a variation tree diff.
+- [DiffTree](https://variantsync.github.io/DiffDetective/docs/javadoc/org/variantsync/diffdetective/diff/difftree/DiffTree.html) and [DiffNode](https://variantsync.github.io/DiffDetective/docs/javadoc/org/variantsync/diffdetective/diff/difftree/DiffNode.html) implement variation diffs from our paper. A variation diff is represented by an instance of the `DiffTree` class. It stores the root node of the diff and offers various methods to parse, traverse, and analyze variation diffs. `DiffNode`s represent individual nodes within a variation diff.
 - [Validation](https://variantsync.github.io/DiffDetective/docs/javadoc/org/variantsync/diffdetective/validation/Validation.html) contains the main method for our validation.
-- [ProposedElementaryPatterns](https://variantsync.github.io/DiffDetective/docs/javadoc/org/variantsync/diffdetective/pattern/elementary/proposed/ProposedElementaryPatterns.html) holds the catalog of nine edit patterns we proposed in our paper. It implements the interface [ElementaryPatternCatalogue](https://variantsync.github.io/DiffDetective/docs/javadoc/org/variantsync/diffdetective/pattern/elementary/ElementaryPatternCatalogue.html), which allows to define custom pattern catalogs.
+- [ProposedEditClasses](https://variantsync.github.io/DiffDetective/docs/javadoc/org/variantsync/diffdetective/editclass/proposed/ProposedEditClasses.html) holds the catalog of the nine edit classes we proposed in our paper. It implements the interface [EditClassCatalogue](https://variantsync.github.io/DiffDetective/docs/javadoc/org/variantsync/diffdetective/editclass/EditClassCatalogue.html), which allows to define custom edit classifications.
 - [BooleanAbstraction](https://variantsync.github.io/DiffDetective/docs/javadoc/org/variantsync/diffdetective/feature/BooleanAbstraction.html) contains data and methods for boolean abstraction of higher-order logic formulas. We use this for macro parsing.
-- [GitDiffer](https://variantsync.github.io/DiffDetective/docs/javadoc/org/variantsync/diffdetective/diff/GitDiffer.html) may parse the history of a git repository to variation tree diffs.
+- [GitDiffer](https://variantsync.github.io/DiffDetective/docs/javadoc/org/variantsync/diffdetective/diff/GitDiffer.html) may parse the history of a git repository to variation diffs.
 - The [datasets](https://variantsync.github.io/DiffDetective/docs/javadoc/org/variantsync/diffdetective/datasets/package-summary.html) package contains various classes for describing and loading datasets.
 
 ## 2. Appendix
 
 Our [appendix][appendix] consists of:
 1. An extended formalization of our concepts in the [Haskell][haskell] programming language. The corresponding source code is also part of this replication package (see below).
-2. The proofs for (a) the completeness of variation tree diffs to represent edits to variation trees, and (b) the completeness and unambiguity of our elementary edit patterns.
-3. An inspection of edit patterns from related work to show that existing patterns are either composite patterns built from our elementary patterns or similar to our elementary patterns. The used diffs of these patterns can also be found in [docs/compositepatterns](docs/compositepatterns).
+2. The proofs for (a) the completeness of variation diffs to represent edits to variation trees, and (b) the completeness and unambiguity of our edit classes.
+3. An inspection of edit patterns from related work to show that existing patterns are either composite patterns built from our edit classes or similar to one of our edit classes. The used diffs of these patterns can also be found in [docs/compositepatterns](docs/compositepatterns).
 4. The complete results of our validation for all 44 datasets.
 
 ## 3. Haskell Formalization
 The extended formalization is a [Haskell][haskell] library in the [`proofs`](proofs) subdirectory.
 Since the `proofs` library is its own software project, we provide a separate documentation of requirements and installation instructions within the projects subdirectory.
-Instructions for manually installing Stack are given in [proofs/REQUIREMENTS.md](proofs/REQUIREMENTS.md).
+Requirements and instructions for setting up the build environment (Stack) are given in [proofs/REQUIREMENTS.md](proofs/REQUIREMENTS.md).
 How to build our library and how to run the example is described in the [proofs/INSTALL.md](proofs/INSTALL.md).
 
 
 ## 4. Dataset Overview
 ### 4.1 Open-Source Repositories
 We provide an overview of the used 44 open-source preprocessor-based software product lines in the [docs/datasets.md][dataset] file.
-As described in our paper in Section 5.1 this list contains all systems that were studied by Liebig et al., extended by four new subject systems (Busybox, Marlin, LibSSH, Godot).
+As described in our paper in Section 5.1, this list contains all systems that were studied by Liebig et al., extended by four new subject systems (Busybox, Marlin, LibSSH, Godot).
 We provide updated links for each system's repository.
 
 ### 4.2 Forked Repositories for Replication

REQUIREMENTS.md

@@ -5,7 +5,7 @@ None
 ## Software Requirements
 
 We do not require a certain operating system or prepared environment.
-We tested our setup on Windows 10, WSL2, and Manjaro.
+The setup is tested on Windows 10, WSL2, Manjaro, Ubuntu, and MacOS Monterey.
 
 To run DiffDetective, JDK16, and Maven are required.
 Dependencies to other packages are documented in the maven build file ([pom.xml](pom.xml)) and are handled automatically by Maven.

STATUS.md

@@ -3,7 +3,7 @@
 The artifact for the paper _Classifying Edits to Variability in Source Code_ consists of four parts:
 
 1. **DiffDetective**: For our validation, we built DiffDetective, a java library and command-line tool to classify edits to variability in git histories of preprocessor-based software product lines. 
-  DiffDetective is the main artifact used to replicate the validation for our paper (see Section 5).
+  DiffDetective is the main artifact used to replicate the validation of our paper (see Section 5).
   DiffDetective is self-contained in that it does not require or depend on in-depth knowledge on the theoretical foundation of our work.
   Practitioners and researches are free to ignore the appendix as well as the haskell formalization and may use DiffDetective out-of-the-box.
 2. **Appendix**: The appendix of our paper is given in PDF format in the file [`appendix.pdf`][ddappendix].
@@ -22,18 +22,18 @@ DiffDetective is designed as a library that offers reusable functionality.
 Researchers and practitioners can use our DiffDetective library to build on our theory and results (e.g., for future prototypes to study the evolution of variability in source code).
 
 DiffDetective offers various features, including but not limited to:
-parsing variation tree diffs from unix diffs, obtaining variation tree diffs for certain patches and commits, matching elementary edit patterns on variation tree diffs, defining custom classifications, rendering, traversing, and transforming variation tree diffs, various de-/serialization methods, and running analyses for the git histories of C preprocessor-based software product lines. We documented each part of the library and provide a [javadoc website][dddocumentation] within the repository.
+parsing variation diffs from unix diffs, obtaining variation diffs for certain patches and commits, classifying edits in variation diffs, defining custom classifications, rendering, traversing, and transforming variation diffs, various de-/serialization methods, and running analyses for the git histories of C preprocessor-based software product lines. We documented each part of the library and provide a [javadoc website][dddocumentation] within the repository.
 Moreover, our validation (see _replicability_ above) may also be run on any custom dataset as described in our [README.md][ddreadme].
 
 ### **Extended Formal Specification**
 The [`proofs`][ddproofs] Haskell project provides an extended formal specification of our theory.
 Its main purpose is to document the theory and its extensions to serve as a reference for the proofs in our appendix.
 Yet, the project can also be used as a library to reason on variation trees and diffs in Haskell projects.
-The library is accompanied by a small demo application that shows an example test case for our proof of completeness by creating a variation tree diff from two variation trees and re-projecting them.
+The library is accompanied by a small demo application that shows an example test case for our proof of completeness by creating a variation diff from two variation trees and re-projecting them.
 The `proofs` project is described in detail in our appendix.
 
 ## Claims
-We claim the _Artifacts Available_ badge as we made our artefacts publicly available on [Github][ddgithub] and [Zenodo][ddzenodo] with an [open-source license][ddlicense]. All [44 input datasets][ddforks] are open-source projects and publicly available.
+We claim the _Artifacts Available_ badge as we made our artifacts publicly available on [Github][ddgithub] and [Zenodo][ddzenodo] with an [open-source license][ddlicense]. All [44 input datasets][ddforks] are open-source projects and publicly available.
 
 We claim the _Artifacts Evaluated Reusable_ badge as we implemented DiffDetective as a reusable library (see above).
 Furthermore, both DiffDetective and our Haskell formalization serve as reference implementations if researchers or practitioners want to reimplement our theory in other programming languages.