Analysis of Code Repositories

Director:Philippsen, M.
Beginning:January 1, 2010
Coworker:Dotzler, G.; Kamp, M.; Kreutzer, P.
Description:

Software developers often modify their projects in a similar or repetitive way. The reasons for these changes include the adoption of a changed interface to a library, the correction of mistakes in functionally similar components, or the parallelization of sequential parts of a program. If developers have to perform the necessary changes on their own, the modifications can easily introduce errors, for example due to a missed change location. Therefore, an automatic technique is desireable that identifies similar changes and uses this knowledge to support developers with further modifications.
Extraction of Code-Changes
In 2017, we developed a new code recommendation tool called ARES (Accurate REcommendation System). It creates more accurate recommendation compared to previous tools as its algorithms take care of code movements during pattern and recommendation creation. The foundation of ARES lies in the comparison of two versions of the same program. It extracts the changes between the two versions and creates patterns based on the changed methods. ARES uses these patterns to suggest similar changes for the source code of different programs automatically.
The extraction of code changes is based on trees. In 2016 we developed (and visibly published) a new tree-based algorithm (MTDIFF) that improves the accuracy of the change extraction.
Symbolic Execution of Code-Fragments
In 2014 we developed a new symbolic code execution engine called SYFEX to determine the behavioral similarity of two code fragments. In this way we aim to improve the quality of the recommendations. Depending on the number and the generality of the patterns in the database, it is possible that without the new engine SIFE generates some unfitting recommendations. To present only the fitting recommendations to the developers, we compare the summary of the semantics/behavior of the recommendation with summary of the semantics/behavior of the database pattern. If both differ too severely, our tool drops the recommendation from the results. The distinctive features of SYFEX are its applicability to isolated code fragments and its automatic configuration that does not require any human interaction.
In 2015 SYFEX was refined and applied to code fragments from the repositories of different software projects. In 2016 we investigated to which extend SYFEX can be used to gauge the semantic similarity of submissions for a programming contest. In 2017 and 2018 we optimized the implementation of SYFEX. We also began collecting a data set of semantically similar methods from open source repositories.
Detection of Semantically Similar Code Fragments
SYFEX computes the semantic similarity of two code fragments. Therefore, it allows to identify pairs or groups of semantically similar code fragments (semantic clones). However, the high runtime of SYFEX (and similar tools) limit their applicability to larger software projects. In 2016, we started the development of a technique to accelerate the detection of semantically similar code fragments. The technique is based on so-called base comparators that compare two code fragments using a single criterion (e.g., the number of used control structures or the structure of the control flow graph) and that have a low runtime. These base comparators can be combined to form a hierarchy of comparators. To compute the semantic similarity of two code fragments as accurately as possible, we use genetic programming to search for hierarchies that approximate the similarity values as reported by SYFEX for a number of pairs of code fragments. A prototype implementation confirmed that the method is capable of detecting pairs of semantically similar code fragments.
We further improved the implementation of this approach in 2017 and 2018. Additionally, we focused on evaluating the approach with pairs of methods from software repositories and from programming exercises.
Semantic Code Search
The functionality that has to be implemented during the development of a software product is often already available as part of program libraries. It is often advisable to reuse such an implementation instead of rewriting it, for example to reduce the effort for developing and testing the code.
To reuse an implementation that fits the purpose, developers have to find it first. To this end developers already use code search engines on a regular basis. State-of-the-art search engines work on a syntactic level, i.e., the user specifies some keywords or names of variables and methods that should be searched for. However, current approaches do not consider the semantics of the code that the user seeks. As a consequence, relevant but syntactically different implementations often remain undetected ("false negatives") or the results include syntactically similar but semantically irrelevant implementations ("false positives"). The search for code fragments on a semantic level is the subject of current research.
In 2017 we began the development of a new method for semantic code search. The user specifies the desired functionality in terms of input/output examples. A function synthesis algorithm from the literature is then used to create a method that implements the specified functionality as accurately as possible. Using our approach to detect similar code fragments, this synthesized method is then compared to the methods of program libraries to find semantically similar implementations. These implementations are then presented as search results to the user. A first evaluation of our prototypical implementation shows the feasibility and practicability of the approach.
Clustering of Similar Code-Changes
To create generalized change patterns, it is necessary that the set of extracted code changes is split into subsets of changes that are similar to each other. In 2015 this detection of similar code changes was improved and resulted in a new tool, called C3. We developed and evaluated different metrics for a pairwise similarity comparison of the extracted code changes. Subsequently, we evaluated different clustering algorithms known from the literature and implemented new heuristics to automatically choose the respective parameters to replace the previous naive approach for the detection of similar code changes. This clearly improved the results compared to the previous approach, i.e., C3's new techniques detect more groups of similar changes that can be processed by SIFE to generate recommendations.
The aim of the second improvement is to automatically refine the resulting groups of similar code changes. For this purpose we evaluated several machine learning algorithms for outlier detection to remove those code changes that have been spuriously assigned to a group.
In 2016 we implemented a new similarity metric for the comparison of two code changes that essentially considers the textual difference between the changes (as generated, for example, by the Unix tool 'diff'). We published both a paper on C3 and the dataset (consisting of groups of similar changes) that we generated for the evaluation of our tool under an open-source license, see https://github.com/FAU-Inf2/cthree . This dataset can be used as a reference or as input data for future research. In addition, we prototypically extended C3 by techniques for an incremental similarity computation and clustering. This allows us to reuse results from previous runs and to only perform the absolutely necessary work whenever new code changes are added to a software archive.

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