Evaluating and Improving Reverse Engineering Tools

Developers tend to leave some important steps and actions (e.g. properly designing the system's architecture, code review and testing) out of the software development process, and use risky practices (e.g. the copy-paste technique) so that the software can be released as fast as possible. However, these practices may turn out to be critical from the viewpoint of maintainability of the software system. In such cases, a cost-effective solution might be to re-engineer the system. Re-engineering consists of two stages, namely reverse-engineering information from the current system and, based on this information, forward-engineering the system to a new form. In this way, successful re-engineering significantly depends on the reverse engineering phase. Therefore, it is vital to guarantee correctness, and to improve the results of the reverse engineering step. Otherwise, the re-engineering of the software system could fail due to the bad results of reverse engineering. The above issues motivated us to develop a method which extends and improves one of our reverse engineering tools, and to develop benchmarks and to perform experiments on evaluating and comparing reverse engineering tools

Visual Analytics in Software Maintenance:Challenges and Opportunities

Visual analytics (VA) is an emerging science at the crossroads of data and information visualization, graphics, data min-ing, and knowledge representation, with many successful applications in engineering, business and finance, security, geo-sciences, and e-governance and health. Tools using visualization, data mining, and data analysis are also prominently present in a different field: software maintenance. However, an integrated VA is relatively new for this field. In this paper, we discuss the specific challenges and particularities of applying VA in software engineering, highlight the added value of a VA approach, as distilled by us from several large-scale software engineering industrial projects. 1

BEFRIEND - a benchmark for evaluating reverse engineering tools

Reverse engineering tools analyze the source code of a software system and produce various results, which usually point back to the original source code. Such tools are e.g. design pattern miners, duplicated code detectors and coding rule violation checkers. Most of the time these tools present their results in different formats, which makes them very difficult to compare. In this paper, we present work in progress towards implementing a benchmark called BEFRIEND (BEnchmark For Reverse engInEering tools workiNg on source coDe) with which the outputs of reverse engineering tools can be easily and efficiently evaluated and compared. It supports different kinds of tool families, programming languages and software systems, and it enables the users to define their own evaluation criteria. Furthermore, it is a freely available web-application open to the community. We hope that in the future it will be accepted and used by the community members to evaluate and compare their tools with each other

Mining domain-specific edit operations from model repositories with applications to semantic lifting of model differences and change profiling

Model transformations are central to model-driven software development. Applications of model transformations include creating models, handling model co-evolution, model merging, and understanding model evolution. In the past, various (semi-) automatic approaches to derive model transformations from meta-models or from examples have been proposed. These approaches require time-consuming handcrafting or the recording of concrete examples, or they are unable to derive complex transformations. We propose a novel unsupervised approach, called Ockham, which is able to learn edit operations from model histories in model repositories. Ockham is based on the idea that meaningful domain-specifc edit operations are the ones that compress the model diferences. It employs frequent subgraph mining to discover frequent structures in model diference graphs. We evaluate our approach in two controlled experiments and one real-world case study of a large-scale industrial model-driven architecture project in the railway domain. We found that our approach is able to discover frequent edit operations that have actually been applied before. Furthermore, Ockham is able to extract edit operations that are meaningful—in the sense of explaining model diferences through the edit operations they comprise—to practitioners in an industrial setting. We also discuss use cases (i.e., semantic lifting of model diferences and change profles) for the discovered edit operations in this industrial setting. We fnd that the edit operations discovered by Ockham can be used to better understand and simulate the evolution of models

A Survey on Design Pattern Recovery Techniques

The evaluation of design pattern recovery techniques and tools is significant as numbers of emergent techniques are presented and used in the past to recover patterns from source code of legacy applications. The problem of very diverse precision and recall values extracted by different pattern recovery techniques and tools on the same examined applications is not investigated thoroughly. It is very desirable to compare features of existing techniques as abundance of techniques supplemented with different tools has been presented in the last decade. We believe that new innovations for this discipline can be based on the empirical evaluation of existing techniques. The selected techniques cover the whole spectrum of state of the art research in design pattern recovery. The major contribution of this paper is a comprehensive discussion on state of the art in design pattern recovery research in the last decade followed by a proposed framework for classification and evaluation of existing design pattern recovery techniques. Finally we listed our observations as lessons learned which hamper design pattern recovery research and these observations can be used for future research directions and guidelines for this discipline