Dynamically typed languages

Dynamically typed languages such as Python and Ruby have experienced a rapid grown in popularity in recent times. However, there is much confusion as to what makes these languages interesting relative to statically typed languages, and little knowledge of their rich history. In this chapter I explore the general topic of dynamically typed languages, how they differ from statically typed languages, their history, and their defining features

A heuristic-based approach to code-smell detection

Encapsulation and data hiding are central tenets of the object oriented paradigm. Deciding what data and behaviour to form into a class and where to draw the line between its public and private details can make the difference between a class that is an understandable, flexible and reusable abstraction and one which is not. This decision is a difficult one and may easily result in poor encapsulation which can then have serious implications for a number of system qualities. It is often hard to identify such encapsulation problems within large software systems until they cause a maintenance problem (which is usually too late) and attempting to perform such analysis manually can also be tedious and error prone. Two of the common encapsulation problems that can arise as a consequence of this decomposition process are data classes and god classes. Typically, these two problems occur together – data classes are lacking in functionality that has typically been sucked into an over-complicated and domineering god class. This paper describes the architecture of a tool which automatically detects data and god classes that has been developed as a plug-in for the Eclipse IDE. The technique has been evaluated in a controlled study on two large open source systems which compare the tool results to similar work by Marinescu, who employs a metrics-based approach to detecting such features. The study provides some valuable insights into the strengths and weaknesses of the two approache

Dynamically typed languages.

Dynamically typed languages such as Python and Ruby have experienced a rapid grown in popularity in recent times. However, there is much confusion as to what makes these languages interesting relative to statically typed languages, and little knowledge of their rich history. In this chapter I explore the general topic of dynamically typed languages, how they differ from statically typed languages, their history, and their defining features

Quality-Aware Tooling

Programming is a fascinating activity that can yield results capable of changing people lives by automating daily tasks or even completely reimagining how we perform certain activities. Such a great power comes with a handful of challenges, with software maintainability being one of them. Maintainability cannot be validated by executing the program but has to be assessed by analyzing the codebase. This tedious task can be also automated by the means of software development. Programs called static analyzers can process source code and try to detect suspicious patterns. While these programs were proven to be useful, there is also an evidence that they are not used in practice. In this dissertation we discuss the concept of quality-aware tooling —- an approach that seeks a promotion of static analysis by seamlessly integrating it into development tools. We describe our experience of applying quality-aware tooling on a core distribution of a development environment. Our main focus is to provide live quality feedback in the code editor, but we also integrate static analysis into other tools based on our code quality model. We analyzed the attitude of the developers towards the integrated static analysis and assessed the impact of the integration on the development ecosystem. As a result 90% of software developers find the live feedback useful, quality rules received an overhaul to better match the contemporary development practices, and some developers even experimented with a custom analysis implementations. We discovered that live feedback helped developers to avoid dangerous mistakes, saved time, and taught valuable concepts. But most importantly we changed the developers' attitude towards static analysis from viewing it as just another tool to seeing it as an integral part of their toolset

Structured Review of Code Clone Literature

This report presents the results of a structured review of code clone literature. The aim of the review is to assemble a conceptual model of clone-related concepts which helps us to reason about clones. This conceptual model unifies clone concepts from a wide range of literature, so that findings about clones can be compared with each other

CentiJ: An RMI Code Generator

The CentiJ system synthesizes Java source code that funnels invocations through an RMI (Remote Method Invocation) based transport layer for distributed computation. The technique generates bridge pattern code (i.e., interfaces and proxies) that automate the creation of virtual proxies for message forwarding. We examine the tradeoffs between bridge implementations based on manual static delegation, automatic static delegation, and dynamic proxy classes. Advantages of the CentiJ technique include improved performance, type safety, transparency, predictability, flexibility and reliability. We then look at various methods for solving the disambiguation problem that arises when delegates have conflicting method signatures. Disambiguation can be automatic, semi-automatic or manual. CentiJ can automatically create a class that alters the interface to the bridge (using the adapter pattern)

30 Years of Software Refactoring Research: A Systematic Literature Review

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/155872/4/30YRefactoring.pd

30 Years of Software Refactoring Research:A Systematic Literature Review

Due to the growing complexity of software systems, there has been a dramatic increase and industry demand for tools and techniques on software refactoring in the last ten years, defined traditionally as a set of program transformations intended to improve the system design while preserving the behavior. Refactoring studies are expanded beyond code-level restructuring to be applied at different levels (architecture, model, requirements, etc.), adopted in many domains beyond the object-oriented paradigm (cloud computing, mobile, web, etc.), used in industrial settings and considered objectives beyond improving the design to include other non-functional requirements (e.g., improve performance, security, etc.). Thus, challenges to be addressed by refactoring work are, nowadays, beyond code transformation to include, but not limited to, scheduling the opportune time to carry refactoring, recommendations of specific refactoring activities, detection of refactoring opportunities, and testing the correctness of applied refactorings. Therefore, the refactoring research efforts are fragmented over several research communities, various domains, and objectives. To structure the field and existing research results, this paper provides a systematic literature review and analyzes the results of 3183 research papers on refactoring covering the last three decades to offer the most scalable and comprehensive literature review of existing refactoring research studies. Based on this survey, we created a taxonomy to classify the existing research, identified research trends, and highlighted gaps in the literature and avenues for further research.Comment: 23 page

BioSmalltalk: a pure object system and library for bioinformatics

We have developed BioSmalltalk, a new environment system for pure object-oriented bioinformatics programming. Adaptive end-user programming systems tend to become more important for discovering biological knowledge, as is demonstrated by the emergence of open-source programming toolkits for bioinformatics in the past years. Our software is intended to bridge the gap between bioscientists and rapid software prototyping while preserving the possibility of scaling to whole-system biology applications. BioSmalltalk performs better in terms of execution time and memory usage than Biopython and BioPerl for some classical situations.Availability: BioSmalltalk is cross-platform and freely available (MIT license) through the Google Project Hosting at http://code.google.com/p/biosmalltalk Supplementary information: Supplementary data are available at Bioinformatics online.Instituto de Genética Veterinari

Automatic Test Generation for Space

The European Space Agency (ESA) uses an engine to perform tests in the Ground Segment infrastructure, specially the Operational Simulator. This engine uses many different tools to ensure the development of regression testing infrastructure and these tests perform black-box testing to the C++ simulator implementation. VST (VisionSpace Technologies) is one of the companies that provides these services to ESA and they need a tool to infer automatically tests from the existing C++ code, instead of writing manually scripts to perform tests. With this motivation in mind, this paper explores automatic testing approaches and tools in order to propose a system that satisfies VST needs