Code search via topic-enriched dependence graph matching

Abstract—Source code contains textual, structural, and seman-tic information, which can all be leveraged for effective search. Some studies have proposed semantic code search where users can specify query topics in a natural language. Other studies can search through system dependence graphs. In this paper, we propose a semantic dependence search engine that integrates both kinds of techniques and can retrieve code snippets based on expressive user queries describing both topics and dependencies. Users can specify their search targets in a free form format describing desired topics (i.e., high-level semantic or functionality of the target code); a specialized graph query language allows users to describe low-level data and control dependencies in code and thus helps to refine the queries described in the free format. Our empirical evaluation on a number of software maintenance tasks shows that our search engine can efficiently locate desired code fragments accurately. Keywords-Code search; topic modelling; dependence graphs; I

Concern localization using information retrieval: An empirical study on Linux kernel

10.1109/WCRE.2011.72Proceedings - Working Conference on Reverse Engineering, WCRE92-9

Real-Time Reflexion Modelling in architecture reconciliation: A multi case study

Context Reflexion Modelling is considered one of the more successful approaches to architecture reconciliation. Empirical studies strongly suggest that professional developers involved in real-life industrial projects find the information provided by variants of this approach useful and insightful, but the degree to which it resolves architecture conformance issues is still unclear. Objective This paper aims to assess the level of architecture conformance achieved by professional architects using Reflexion Modelling, and to determine how the approach could be extended to improve its suitability for this task. Method An in vivo, multi-case-study protocol was adopted across five software systems, from four different financial services organizations. Think-aloud, video-tape and interview data from professional architects involved in Reflexion Modelling sessions were analysed qualitatively. Results This study showed that (at least) four months after the Reflexion Modelling sessions less than 50% of the architectural violations identified were removed. The majority of participants who did remove violations favoured changes to the architectural model rather than to the code. Participants seemed to work off two specific architectural templates, and interactively explored their architectural model to focus in on the causes of violations, and to assess the ramifications of potential code changes. They expressed a desire for dependency analysis beyond static-source-code analysis and scalable visualizations. Conclusion The findings support several interesting usage-in-practice traits, previously hinted at in the literature. These include (1) the iterative analysis of systems through Reflexion models, as a precursor to possible code change or as a focusing mechanism to identify the location of architecture conformance issues, (2) the extension of the approach with respect to dependency analysis of software systems and architectural modelling templates, (3) improved visualization support and (4) the insight that identification of architectural violations in itself does not lead to their removal in the majority of instances.This work was supported, in part, by Science Foundation Ireland Grants 12/IP/1351 and 10/CE/I1855 to Lero – the Irish Software Engineering Research Centre (www.lero.ie) and by the University of Brighton under the Rising Star Scheme awarded to Nour Ali

Recovering Container Class Types in C++ Binaries

We present TIARA, a novel approach to recovering container classes in c++ binaries. Given a variable address in a c++ binary, TIARA first applies a new type-relevant slicing algorithm incorporated with a decay function, TSLICE, to obtain an inter-procedural forward slice of instructions expressed as a CFG to summarize how the variable is used in the binary (as our primary contribution). TIARA then makes use of a GCN (Graph Convolutional Network) to learn and predict the container type for the variable (as our secondary contribution). According to our evaluation, TIARA can advance the state of the art in inferring commonly used container types in a set of eight large real-world COTS c++ binaries efficiently (in terms of the overall analysis time) and effectively (in terms of precision, recall and F1 score)

Automated Analysis in Feature Modelling and Product Configuration

The automated analysis of feature models is one of the thriving topics of research in the software product line and variability management communities that has attracted more attention in the last years. A recent literature review reported that more than 30 analysis operations have been identi ed and di erent analysis mechanisms have been proposed. Product con guration is a well established research eld with more than 30 years of successful applications in di erent industrial domains. Our hypothesis, that is not really new, is that these two independent areas of research have interesting synergies that have not been fully explored. To try to explore the potential synergies systematically, in this paper we provide a rapid review to bring together these previously disparate streams of work. We de ne a set of research questions and give a preliminary answer to some of them. We conclude that there are many research opportunities in the synergy of these independent areas.Ministerio de Ciencia e Innovación TIN2009- 07366Junta de Andalucía TIC-590

Genetic Improvement of Software: From Program Landscapes to the Automatic Improvement of a Live System

In today’s technology driven society, software is becoming increasingly important in more areas of our lives. The domain of software extends beyond the obvious domain of computers, tablets, and mobile phones. Smart devices and the internet-of-things have inspired the integra- tion of digital and computational technology into objects that some of us would never have guessed could be possible or even necessary. Fridges and freezers connected to social media sites, a toaster activated with a mobile phone, physical buttons for shopping, and verbally asking smart speakers to order a meal to be delivered. This is the world we live in and it is an exciting time for software engineers and computer scientists. The sheer volume of code that is currently in use has long since outgrown beyond the point of any hope for proper manual maintenance. The rate of which mobile application stores such as Google’s and Apple’s have expanded is astounding. The research presented here aims to shed a light on an emerging field of research, called Genetic Improvement ( GI ) of software. It is a methodology to change program code to improve existing software. This thesis details a framework for GI that is then applied to explore fitness landscape of bug fixing Python software, reduce execution time in a C ++ program, and integrated into a live system. We show that software is generally not fragile and although fitness landscapes for GI are flat they are not impossible to search in. This conclusion applies equally to bug fixing in small programs as well as execution time improvements. The framework’s application is shown to be transportable between programming languages with minimal effort. Additionally, it can be easily integrated into a system that runs a live web service. The work within this thesis was funded by EPSRC grant EP/J017515/1 through the DAASE project