An Approach to Software Evolution Based on Semantic Change

2000

Understanding the impact of software changes has been a challenge since software systems were first developed. With the increasing size and complexity of systems, this problem has become more difficult. There are many ways to identify change impact from the plethora of software artifacts produced during development and maintenance. We present the analysis of the software development process using change

Electronic Notes in Theoretical Computer Science, 2007

Software evolution research is limited by the amount of information available to researchers: Current version control tools do not store all the information generated by developers. They do not record every intermediate version of the system issued, but only snapshots taken when a developer commits source code into the repository. Additionally, most software evolution analysis tools are not a part of the day-to-day programming activities, because analysis tools are resource intensive and not integrated in development environments. We propose to model development information as change operations that we retrieve directly from the programming environment the developer is using, while he is effecting changes to the system. This accurate and incremental information opens new ways for both developers and researchers to explore and evolve complex systems.

Proceedings Sixth Asia Pacific Software Engineering Conference (ASPEC'99) (Cat. No.PR00509), 1999

One of the characteristics of large software systems is that they evolve over time. Evolution patterns include modijications related to the implementation, intelfaces and the overall system structure. Consequently, system understanding and maintainability tend to degrade over time unless particular attention is paid to measure, assess and evaluate the effects of the evolution activities. Traditionally, the assessment of evolution activities has focused on the architectural level. Howevel; in many cases it is easier to extract low-level program information from the Abstract Syntax Tree rather than to discover the full architecture of a large legacy system. This paper presents techniques for analyzing the evolution of large systems even in cases where no complete architectural views of the system exist, from information obtained solely from the AST We present experimental results by analyzing the evolution patterns across different versions, of two popular systems, the Apache Web servec and the Bash shell.

2000

More than 90% of the cost of software is due to maintenance and evolution1. We claim that the commonly held vision of a software as a set of files, and its history as a set of versions does not accurately represent the phenomenon of software evolution: Software development is an incremental process more complex than simply writing lines of text.

2013 28th IEEE/ACM International Conference on Automated Software Engineering (ASE), 2013

In this paper, we present a large-scale study of repetitiveness of code changes in software evolution. We collected a large data set of 2,841 Java projects, with 1.7 billion source lines of code (SLOC) at the latest revisions, 1.8 million code change revisions (0.4 million fixes), 6.2 million changed files, and 2.5 billion changed SLOCs. A change is considered repeated within or cross-project if it matches another change having occurred in the history of the project or another project, respectively. We report the following important findings. First, repetitiveness of changes could be as high as 70-100% at small sizes and decreases exponentially as size increases. Second, repetitiveness is higher and more stable in the cross-project setting than in the within-project one. Third, fixing changes repeat similarly to general changes. Importantly, learning code changes and recommending them in software evolution is beneficial with accuracy for top-1 recommendation of over 30% and top-3 of nearly 35%. Repeated fixing changes could also be useful for automatic program repair. Disciplines Computer Sciences | Software Engineering Comments

Proceedings of the 4th …, 2001

Case-studies are extremely popular in rapidly evolving research disciplines such as software engineering because they allow for a quick but fair assessment of new techniques. Unfortunately, a proper experimental set-up is rarely the case: all too often case-studies are based on a single small toyexample chosen to favour the technique under study. Such lack of scientific rigor prevents fair evaluation and has serious consequences for the credibility of our field. In this paper, we propose to use a representative set of cases as a benchmark for comparing various techniques dealing with software evolution. We hope that this proposal will launch a consensus building process that eventually must lead to a scientifically sound validation method for researchers investigating reverseand re-engineering techniques.

2010 17th Working Conference on Reverse Engineering, 2010

The paper presents an approach that combines conceptual and evolutionary techniques to support change impact analysis in source code. Information Retrieval (IR) is used to derive conceptual couplings from the source code in a single version (release) of a software system. Evolutionary couplings are mined from source code commits. The premise is that such combined methods provide improvements to the accuracy of impact sets. A rigorous empirical assessment on the changes of the open source systems Apache httpd, ArgoUML, iBatis, and KOffice is also reported. The results show that a combination of these two techniques, across several cut points, provides statistically significant improvements in accuracy over either of the two techniques used independently. Improvements in recall values of up to 20% over the conceptual technique in KOffice and up to 45% over the evolutionary technique in iBatis were reported.

2010

Proc. Int. Workshop on Principles of Software Evolution (IWPSE’98), Kyoto, Japan, 1998

1. MOTIVATION Evolution is omnipresent in software engineering due to constantly changing requirements, technological advances, bug fixes, software maintenance, new insights in the problem domain, and so on. Moreover, changes occur at every level of detail, and in every phase of the software life-cycle, ranging from requirements specification over analysis and design to low-level code. Many problems can arise during evolution. When a software component evolves, other components that depend on it may give rise to evolution conflicts when ...

Proc. Workshop on …, 2003

Previous taxonomies of software evolution have focused on the purpose of the change (i.e., the why) rather than the underlying mechanisms. This paper proposes a taxonomy of software evolution based on the characterizing mechanisms of change and the factors that influence these mechanisms. The taxonomy is organized into the following logical groupings: temporal properties, objects of change, system properties, and change support.