Application of Constructive Modeling and Process Mining Approaches to the Study of Source Code Development in Software Engineering Courses

We present an approach of constructing a source code history for a modern code review. Practically, it is supposed to be used in programming training, especially within initial stages. The developed constructor uses constructive-synthesizing modeling tools to classify a source code history by fine-grained changes and to construct an event log file aimed to provide information on students’ coding process. Current research applies Process Mining techniques to the software engineering domain to identify software engineering skills. By better understanding of the way students design programs, we will help novices learn programming. This research provides an innovative method of using code and development process review in teaching programming skills and is aimed to encourage using code review and monitoring coding practice in educational purposes. The standard method of evaluation takes into consideration only a final result, which doesn’t meet modern requirements of teaching programming

Deep Learning for Code Intelligence: Survey, Benchmark and Toolkit

Code intelligence leverages machine learning techniques to extract knowledge from extensive code corpora, with the aim of developing intelligent tools to improve the quality and productivity of computer programming. Currently, there is already a thriving research community focusing on code intelligence, with efforts ranging from software engineering, machine learning, data mining, natural language processing, and programming languages. In this paper, we conduct a comprehensive literature review on deep learning for code intelligence, from the aspects of code representation learning, deep learning techniques, and application tasks. We also benchmark several state-of-the-art neural models for code intelligence, and provide an open-source toolkit tailored for the rapid prototyping of deep-learning-based code intelligence models. In particular, we inspect the existing code intelligence models under the basis of code representation learning, and provide a comprehensive overview to enhance comprehension of the present state of code intelligence. Furthermore, we publicly release the source code and data resources to provide the community with a ready-to-use benchmark, which can facilitate the evaluation and comparison of existing and future code intelligence models (https://xcodemind.github.io). At last, we also point out several challenging and promising directions for future research

Metrics for Aspect Mining Visualization

Aspect oriented programming has over the last decade become the subject of intense research within the domain of software engineering. Aspect mining, which is concerned with identification of cross cutting concerns in legacy software, is an important part of this domain. Aspect refactoring takes the identified cross cutting concerns and converts these into new software constructs called aspects. Software that have been transformed using this process becomes more modularized and easier to comprehend and maintain. The first attempts at mining for aspects were dominated by manual searching and parsing through source code using simple tools. More sophisticated techniques have since emerged including evaluation of execution traces, code clone detection, program slicing, dynamic analysis, and use of various clustering techniques. The focus of most studies has been to maximize aspect mining performance measured by various metrics including those of aspect mining precision and recall. Other metrics have been developed and used to compare the various aspect mining techniques with each other. Aspect mining automation and presentation of aspect mining results has received less attention. Automation of aspect mining and presentation of results conducive to aspect refactoring is important if this research is going to be helpful to software developers. This research showed that aspect mining can be automated. A tool was developed which performed automated aspect mining and visualization of identified cross cutting concerns. This research took a different approach to aspect mining than most aspect mining research by recognizing that many different categories of cross cutting concerns exist and by taking this into account in the mining process. Many different aspect mining techniques have been developed over time, some of which are complementary. This study was different than most aspect mining research in that multiple complementary aspect mining algorithms was used in the aspect mining and visualization process

Java-ML: a machine learning library

Java-ML is a collection of machine learning and data mining algorithms, which aims to be a readily usable and easily extensible API for both software developers and research scientists. The interfaces for each type of algorithm are kept simple and algorithms strictly follow their respective interface. Comparing different classifiers or clustering algorithms is therefore straightforward, and implementing new algorithms is also easy. The implementations of the algorithms are clearly written, properly documented and can thus be used as a reference. The library is written in Java and is available from http://java-ml.sourceforge.net/ under the GNU GPL license

Recursion Aware Modeling and Discovery For Hierarchical Software Event Log Analysis (Extended)

This extended paper presents 1) a novel hierarchy and recursion extension to the process tree model; and 2) the first, recursion aware process model discovery technique that leverages hierarchical information in event logs, typically available for software systems. This technique allows us to analyze the operational processes of software systems under real-life conditions at multiple levels of granularity. The work can be positioned in-between reverse engineering and process mining. An implementation of the proposed approach is available as a ProM plugin. Experimental results based on real-life (software) event logs demonstrate the feasibility and usefulness of the approach and show the huge potential to speed up discovery by exploiting the available hierarchy.Comment: Extended version (14 pages total) of the paper Recursion Aware Modeling and Discovery For Hierarchical Software Event Log Analysis. This Technical Report version includes the guarantee proofs for the proposed discovery algorithm