Preserving Instance State during Refactorings in Live Environments

International audienceAn important activity of software evolution consists in applying refactorings to enhance the quality of the code without changing its behaviour. Having a proper refactoring tool is a must-to in any professional development environment. In addition, live programming allows faster development than the usual edit-compile-debug process. During live programming sessions, the developer can directly manipulate instances and modify the state of the running program. However, when a complex refactoring is performed, instances may be corrupted (i.e., their state is lost). For example, when pushing an instance variable to a superclass there is a moment where the superclass does not have yet acquired the new instance variable and the subclass does not have it any more. It means that the value assigned to this instance variable in existing instances is lost after the refactoring. This problem is not anecdotal since 36% of the refactorings described in Fowler's catalogue corrupt instances when used in a live programming context. There is a need to manually migrate, regenerate or reload instances from persistent sources. This manual fix lowers the usefulness of live programming. In this context of live programming, we propose, AtomicRefactoring, a new solution based on Dynamic Software Update to preserve the state of the application after performing refactorings. We provide a working extension to the existing refactoring tool developed for the language Pharo (a new offspring inheriting from Smalltalk), allowing application developers to perform complex refactorings preserving the live state of the running program

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

Semantic Deltas for Live DSL Environments

Domain-specific languages (DSLs) require IDE support, just like ordinary programming languages. This paper introduces semantic deltas as a foundation for building live DSL environments to bridge the "gulf of evaluation" between DSL code and the running application. Semantic deltas are distinguished from textual or structural deltas in two ways. First, they have meaning in the application domain captured by the DSL. Second, they can be interpreted at runtime so that the behavior of the running system adapts to the evolved DSL code. Semantic deltas have the potential to support back-in-time debugging, application state persistence, version control, retroactive updates and exploring what-if scenarios. I present early experiences in building a live DSL environment and identify areas for future research

Refactoring Practices in the Context of Modern Code Review: An Industrial Case Study at Xerox

Modern code review is a common and essential 2 practice employed in both industrial and open-source projects 3 to improve software quality, share knowledge, and ensure con4 formance with coding standards. During code review, developers 5 may inspect and discuss various changes including refactoring 6 activities before merging code changes in the code base. To date, 7 code review has been extensively studied to explore its general 8 challenges, best practices and outcomes, and socio-technical 9 aspects. However, little is known about how refactoring activities 10 are being reviewed, perceived, and practiced. 11 This study aims to reveal insights into how reviewers develop 12 a decision about accepting or rejecting a submitted refactoring 13 request, and what makes such review challenging. We present an 14 industrial case study with 24 professional developers at Xerox. 15 Particularly, we study the motivations, documentation practices, 16 challenges, verification, and implications of refactoring activities 17 during code review. 18 Our study delivers several important findings. Our results 19 report the lack of a proper procedure to follow by developers 20 when documenting their refactorings for review. Our survey 21 with reviewers has also revealed several difficulties related to 22 understanding the refactoring intent and implications on the 23 functional and non-functional aspects of the software. In light of 24 our findings, we recommended a procedure to properly document 25 refactoring activities, as part of our survey feedback

State of Refactoring Adoption: Towards Better Understanding Developer Perception of Refactoring

Context: Refactoring is the art of improving the structural design of a software system without altering its external behavior. Today, refactoring has become a well-established and disciplined software engineering practice that has attracted a significant amount of research presuming that refactoring is primarily motivated by the need to improve system structures. However, recent studies have shown that developers may incorporate refactoring strategies in other development-related activities that go beyond improving the design especially with the emerging challenges in contemporary software engineering. Unfortunately, these studies are limited to developer interviews and a reduced set of projects. Objective: We aim at exploring how developers document their refactoring activities during the software life cycle. We call such activity Self-Affirmed Refactoring (SAR), which is an indication of the developer-related refactoring events in the commit messages. After that, we propose an approach to identify whether a commit describes developer-related refactoring events, to classify them according to the refactoring common quality improvement categories. To complement this goal, we aim to reveal insights into how reviewers develop a decision about accepting or rejecting a submitted refactoring request, what makes such review challenging, and how to the efficiency of refactoring code review. Method: Our empirically driven study follows a mixture of qualitative and quantitative methods. We text mine refactoring-related documentation, then we develop a refactoring taxonomy, and automatically classify a large set of commits containing refactoring activities, and identify, among the various quality models presented in the literature, the ones that are more in-line with the developer\u27s vision of quality optimization, when they explicitly mention that they are refactoring to improve them to obtain an enhanced understanding of the motivation behind refactoring. After that, we performed an industrial case study with professional developers at Xerox to study the motivations, documentation practices, challenges, verification, and implications of refactoring activities during code review. Result: We introduced SAR taxonomy on how developers document their refactoring strategies in commit messages and proposed a SAR model to automate the detection of refactoring. Our survey with code reviewers has revealed several difficulties related to understanding the refactoring intent and implications on the functional and non-functional aspects of the software. Conclusion: Our SAR taxonomy and model, can work in conjunction with refactoring detectors, to report any early inconsistency between refactoring types and their documentation and can serve as a solid background for various empirical investigations. In light of our findings of the industrial case study, we recommended a procedure to properly document refactoring activities, as part of our survey feedback

Toward an Effective Automated Tracing Process

Traceability is defined as the ability to establish, record, and maintain dependency relations among various software artifacts in a software system, in both a forwards and backwards direction, throughout the multiple phases of the project’s life cycle. The availability of traceability information has been proven vital to several software engineering activities such as program comprehension, impact analysis, feature location, software reuse, and verification and validation (V&V). The research on automated software traceability has noticeably advanced in the past few years. Various methodologies and tools have been proposed in the literature to provide automatic support for establishing and maintaining traceability information in software systems. This movement is motivated by the increasing attention traceability has been receiving as a critical element of any rigorous software development process. However, despite these major advances, traceability implementation and use is still not pervasive in industry. In particular, traceability tools are still far from achieving performance levels that are adequate for practical applications. Such low levels of accuracy require software engineers working with traceability tools to spend a considerable amount of their time verifying the generated traceability information, a process that is often described as tedious, exhaustive, and error-prone. Motivated by these observations, and building upon a growing body of work in this area, in this dissertation we explore several research directions related to enhancing the performance of automated tracing tools and techniques. In particular, our work addresses several issues related to the various aspects of the IR-based automated tracing process, including trace link retrieval, performance enhancement, and the role of the human in the process. Our main objective is to achieve performance levels, in terms of accuracy, efficiency, and usability, that are adequate for practical applications, and ultimately to accomplish a successful technology transfer from research to industry