A Lightweight Visualization of Interprocedural Data-Flow Paths for Source Code Reading

Program Comprehension (ICPC), 2012 IEEE 20th International Conference onDate of Conference:11-13 June 2012Conference Location :Passa

Sawja: Static Analysis Workshop for Java

Static analysis is a powerful technique for automatic verification of programs but raises major engineering challenges when developing a full-fledged analyzer for a realistic language such as Java. This paper describes the Sawja library: a static analysis framework fully compliant with Java 6 which provides OCaml modules for efficiently manipulating Java bytecode programs. We present the main features of the library, including (i) efficient functional data-structures for representing program with implicit sharing and lazy parsing, (ii) an intermediate stack-less representation, and (iii) fast computation and manipulation of complete programs

Identifying Extract Class and Extract Method Refactoring Opportunities Through Analysis of Variable Declarations and Uses

For small software systems, with perhaps a few thousand lines of code, software structure is largely an esthetic issue. When software systems grow large, including perhaps a million or more lines of source code, their structures become much more important. Developing a large system requires teams of developers working in concert to provide a finished product in a reasonable amount of time. That means that many people will read each component to use, test or modify towards accomplishing new features. In the software development life cycle, the maintenance phase is a dominant stage that impacts production cost of the system dramatically. This is mainly because, for a successful system, the maintenance phase lasts until the system\u27s retirement and includes crucial operations such as enhancing performance, fixing newly discovered bugs and adopting/expending the software to meet new user requirements. Moreover, a software component may be modified or fixed by someone who is not the original author of that component. In this case, all the operations conducted during maintenance or initial development may lead to insertion of code into a unit that may be unrelated to the original design concept of that unit. As software systems become large and complex they grow too long to read and understand completely by a single person. After their initial implementations, maintenance operations tend to make the system even less maintainable, increasing the time and effort needed for future maintenance. In this research, we are interested in finding ways to successfully detect code defects and propose solutions to increase the overall maintainability of software systems that are larger than any one person can completely comprehend from its code alone. This process of refactoring software impacts the total production cost of the system positively by improving the quality of software code such as its comprehensibility and readability. To reduce the total development cost for a system, we suggest three main re-factorings. These novel forms of refactoring techniques aim to eliminate code defects such as large classes and long methods. The main goal of these re-factorings is to create smaller and cohesive software units with clear intentions to improve the maintainability of software. We provide analysis and visualization tools to help a user identify candidate code fragments to be extracted as separate unites. With these automation tools, developers do not have to manually inspect a foreign code base to detect possible refactoring opportunities. Through the visual representations we provide, one can observe all suggested re-factorings effectively on large scale software systems and decide whether a particular refactoring needs to be applied. To show the effectiveness of our techniques, we also provide some experiments conducted using these tools and techniques both on our own project\u27s source code and other open-source projects

An Introduction to Programming for Bioscientists: A Python-based Primer

Computing has revolutionized the biological sciences over the past several decades, such that virtually all contemporary research in the biosciences utilizes computer programs. The computational advances have come on many fronts, spurred by fundamental developments in hardware, software, and algorithms. These advances have influenced, and even engendered, a phenomenal array of bioscience fields, including molecular evolution and bioinformatics; genome-, proteome-, transcriptome- and metabolome-wide experimental studies; structural genomics; and atomistic simulations of cellular-scale molecular assemblies as large as ribosomes and intact viruses. In short, much of post-genomic biology is increasingly becoming a form of computational biology. The ability to design and write computer programs is among the most indispensable skills that a modern researcher can cultivate. Python has become a popular programming language in the biosciences, largely because (i) its straightforward semantics and clean syntax make it a readily accessible first language; (ii) it is expressive and well-suited to object-oriented programming, as well as other modern paradigms; and (iii) the many available libraries and third-party toolkits extend the functionality of the core language into virtually every biological domain (sequence and structure analyses, phylogenomics, workflow management systems, etc.). This primer offers a basic introduction to coding, via Python, and it includes concrete examples and exercises to illustrate the language's usage and capabilities; the main text culminates with a final project in structural bioinformatics. A suite of Supplemental Chapters is also provided. Starting with basic concepts, such as that of a 'variable', the Chapters methodically advance the reader to the point of writing a graphical user interface to compute the Hamming distance between two DNA sequences.Comment: 65 pages total, including 45 pages text, 3 figures, 4 tables, numerous exercises, and 19 pages of Supporting Information; currently in press at PLOS Computational Biolog

Weighted system dependence graph

Abstract-In this paper, we present a weighted, hybrid program-dependence model that represents the relevance of highly related, dependent code to assist developer comprehension of the program for multiple software-engineering tasks. Programmers often need to understand the dependencies among program elements, which may exist across multiple modules. Although such dependencies can be gathered from traditional models, such as slices, the scalability of these approaches is often prohibitive for direct, practical use. To address this scalability issue, as well as to assist developer comprehension, we introduce a program model that includes static dependencies as well as information about any number of executions, which inform the weight and relevance of the dependencies. Additionally, classes of executions can be differentiated in such a way as to support multiple software-engineering tasks. We evaluate this weighted, hybrid model for a task that involves exploring the structural context while debugging. The results demonstrate that the new model more effectively reveals relevant failure-correlated code than the static-only model, thus enabling a more scalable exploration or post hoc analysis

Conceptual roles of data in program: analyses and applications

Program comprehension is the prerequisite for many software evolution and maintenance tasks. Currently, the research falls short in addressing how to build tools that can use domain-specific knowledge to provide powerful capabilities for extracting valuable information for facilitating program comprehension. Such capabilities are critical for working with large and complex program where program comprehension often is not possible without the help of domain-specific knowledge.;Our research advances the state-of-art in program analysis techniques based on domain-specific knowledge. The program artifacts including variables and methods are carriers of domain concepts that provide the key to understand programs. Our program analysis is directed by domain knowledge stored as domain-specific rules. Our analysis is iterative and interactive. It is based on flexible inference rules and inter-exchangeable and extensible information storage. We designed and developed a comprehensive software environment SeeCORE based on our knowledge-centric analysis methodology. The SeeCORE tool provides multiple views and abstractions to assist in understanding complex programs. The case studies demonstrate the effectiveness of our method. We demonstrate the flexibility of our approach by analyzing two legacy programs in distinct domains