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Java decompiler using machine translation techniques
Chatzilenas, Christos; Χατζηλένας, Χρήστος; Athens University of Economics and Business, Department of Informatics; Spinellis, Diomidis
A decompiler is a computer program that converts an executable file or low-level/machine language into a format that is understandable to software programmers. Even though a decompiler may not always reconstruct perfectly the original source code, it remains an important tool for reverse engineering of computer software. The process of decompilation is very useful for the recovery of lost source code, for analyzing and understanding software whose code is not available, even for computer security in some cases. In this thesis, in order to address the decompilation problem we transform it to a translation problem which can be solved using machine translation. Two approaches are studied, statistical and neural machine translation, using two open-source tools Moses and OpenNMT, respectively. Maven repositories are retrieved from GitHub in order to form the dataset and an appropriate procedure is used to construct the parallel corpora. In this context experiments in Moses are not successful while the result of translation using neural machine translation, is fairly good. The difference between the decompiler presented in this thesis and existing Java decompilers is the fact that it can translate bytecode snippets. Additionally, this approach can be extended to produce better results by recovering variables, methods, class names and comments. Finally, this study illustrates that the Java source code which is produced from the decompilation procedure is useful. However, extending it may produce better results.
Word embeddings for the software engineering domain
(2018) Efstathiou, Vasiliki; Chatzilenas, Christos; Spinellis, Diomidis
The software development process produces vast amounts of textual data expressed in natural language. Outcomes from the natural language processing community have been adapted in software engineering research for leveraging this rich textual information;these include methods and readily available tools, often furnished with pre–trained models. State of the art pre–trained models however,capture general, common sense knowledge, with limited value when it comes to handling data specific to a specialized domain.There is currently a lack of domain-specific pre–trained models that would further enhance the processing of natural language artefacts related to software engineering. To this end, we release a word2vecmodel trained over 15GB of textual data from Stack Overflow posts.We illustrate how the model disambiguates polysemous words by interpreting them within their software engineering context. In addition, we present examples of fine-grained semantics captured by the model, that imply transferability of these results to diverse,targeted information retrieval tasks in software engineering and motivate for further reuse of the model.