Mircea Lungu

Recovering inter-project dependencies in software ecosystems

In large software systems, knowing the dependencies between modules or components is critical to assess the impact of changes. To recover the dependencies, fact extractors analyze the system as a whole and build the dependency graph, parsing the system down to the statement level. At the level of software ecosystems, which are collections of software projects, the dependencies that need to be recovered reside not only within the individual systems, but also between the libraries, frameworks, and entire software systems that make up the complete ecosystem; scaling issues arise. In this paper we present and evaluate several variants of a lightweight and scalable approach to recover dependencies between the software projects of an ecosystem. We evaluate our recovery algorithms on the Squeak 3.10 Universe, an ecosystem containing more than 200 software projects.

Mondrian: An Agile Visualization Framework

Data visualization is the process of representing data as pictures to support reasoning about the underlying data. For the interpretation to be as easy as possible, we need to be as close as possible to the original data. As most visualization tools have an internal meta-model, which is different from the one for the presented data, they usually need to duplicate the original data to conform to their meta-model. This leads to an increase in the resources needed, increase which is not always justified. In this work we argue for the need of having an engine that is as close as possible to the data and we present our solution of moving the visualization tool to the data, instead of moving the data to the visualization tool. Our solution also emphasizes the necessity of reusing basic blocks to express complex visualizations and allowing the programmer to script the visualization using his preferred tools, rather than a third party format. As a validation of the expressiveness of our framework, we show how we express several already published visualizations and describe the pros and cons of the approach.

The Small Project Observatory: Visualizing software ecosystems

Software evolution research has focused mostly on analyzing the evolution of single software systems. However, it is rarely the case that a project exists as standalone, independent of others. Rather, projects exist in parallel within larger contexts in companies, research groups or even the open-source communities. We call these contexts software ecosystems. In this paper, we present the Small Project Observatory, a prototype tool which aims to support the analysis of software ecosystems through interactive visualization and exploration. We present a case study of exploring an ecosystem using our tool, we describe the architecture of the tool, and we distill lessons learned during the tool-building experience.

How do developers react to API deprecation?: the case of a smalltalk ecosystem

When the Application Programming Interface (API) of a framework or library changes, its clients must be adapted. This change propagation---known as a ripple effect---is a problem that has garnered interest: several approaches have been proposed in the literature to react to these changes. Although studies of ripple effects exist at the single system level, no study has been performed on the actual extent and impact of these API changes in practice, on an entire software ecosystem associated with a community of developers. This paper reports on an empirical study of API deprecations that led to ripple effects across an entire ecosystem. Our case study subject is the development community gravitating around the Squeak and Pharo software ecosystems: seven years of evolution, more than 3,000 contributors, and more than 2,600 distinct systems. We analyzed 577 methods and 186 classes that were deprecated, and answer research questions regarding the frequency, magnitude, duration, adaptation, and consistency of the ripple effects triggered by API changes.

Visualizing Co-Change Information with the Evolution Radar

Software evolution analysis provides a valuable source of information that can be used both to understand a systems design and predict its future development. While for many program comprehension purposes, it is sufficient to model a single version of a system, there are types of information that can only be recovered when the history of a system is taken into account. Logical coupling, the implicit dependency between software artifacts that have been changed together, is an example of such information. Previous research has dealt with low-level couplings between files, leading to an explosion of the data to be analyzed, or has abstracted the logical couplings to the level of modules, leading to a loss of detailed information. In this paper, we present a visualization-based approach that integrates logical coupling information at different levels of abstraction. This facilitates an in-depth analysis of the logical couplings, and at the same time, leads to a characterization of a systems modules in terms of their logical coupling. The presented approach supports the retrospective analysis of a software system and maintenance activities such as restructuring and redocumentation. We illustrate retrospective analysis on two large open-source software systems.

Mondrian: an agile information visualization framework

Data visualization is the process of representing data as pictures to support reasoning about the underlying data. For the interpretation to be as easy as possible, we need to be as close as possible to the original data. As most visualization tools have an internal meta-model, which is different from the one for the presented data, they usually need to duplicate the original data to conform to their meta-model. This leads to an increase in the resources needed, increase which is not always justified. In this work we argue for the need of having an engine that is as close as possible to the data and we present our solution of moving the visualization tool to the data, instead of moving the data to the visualization tool. Our solution also emphasizes the necessity of reusing basic blocks to express complex visualizations and allowing the programmer to script the visualization using his preferred tools, rather than a third party format. As a validation of the expressiveness of our framework, we show how we express several already published visualizations and describe the pros and cons of the approach.

Package patterns for visual architecture recovery

Recovering the architecture is the first step towards reengineering a software system. Many reverse engineering tools use top-down exploration as a way of providing a visual and interactive process for architecture recovery. During the exploration process, the user navigates through various views on the system by choosing from several exploration operations. Although some sequences of these operations lead to views which, from the architectural point of view, are mode relevant than others, current tools do not provide a way of predicting which exploration paths are worth taking and which are not. In this article, we propose a set of package patterns which are used for augmenting the exploration process with information about the worthiness of the various exploration paths. The patterns are defined based on the internal package structure and on the relationships between the package and the other packages in the system. To validate our approach, we verify the relevance of the proposed patterns for real-world systems by analyzing their frequency of occurrence in six open-source software projects

Towards reverse engineering software ecosystems

Reverse engineering is the process of recovering a projectpsilas components and the relationships between them with the goal of creating representations of the project at a higher level of abstraction. When dealing with the large amounts of information that are analyzed during reverse engineering visualization and exploratory navigation are important tools. However, a software system does not exist by itself. Instead, a project is part of a larger software ecosystem of projects that is developed in the context of an organization, a research group of an open-source community. In our work, we argue that reverse engineering an ecosystem is a natural and complementary extension to the traditional single system reverse engineering. We propose a methodology based on visualization, top-down exploration, architecture recovery and software evolution analysis for the reverse engineering software ecosystems. Our methodology starts with visualizing high-level structural and evolutionary aspects of the ecosystem from which the reverse engineer can navigate to views which present architectural aspects of the individual projects. To support our approach we implemented tool support for analyzing the ecosystem level as well as the intra-project level.

On how often code is cloned across repositories

Detecting code duplication in large code bases, or even across project boundaries, is problematic due to the massive amount of data involved. Large-scale clone detection also opens new challenges beyond asking for the provenance of a single clone fragment, such as assessing the prevalence of code clones on the entire code base, and their evolution. We propose a set of lightweight techniques that may scale up to very large amounts of source code in the presence of multiple versions. The common idea behind these techniques is to use bad hashing to get a quick answer. We report on a case study, the Squeaksource ecosystem, which features thousands of software projects, with more than 40 million versions of methods, across more than seven years of evolution. We provide estimates for the prevalence of type-1, type-2, and type-3 clones in Squeaksource.

An approach to software evolution based on semantic change

The analysis of the evolution of software systems is a useful source of information for a variety of activities, such as reverse engineering, maintenance, and predicting the future evolution of these systems. Current software evolution research is mainly based on the information contained in versioning systems such as CVS and SubVersion. But the evolutionary information contained therein is incomplete and of low quality, hence limiting the scope of evolution research. It is incomplete because the historical information is only recorded at the explicit request of the developers (a commit in the classical checkin/checkout model). It is of low quality because the file-based nature of versioning systems leads to a view of software as being a set of files. In this paper we present a novel approach to software evolution analysis which is based on the recording of all semantic changes performed on a system, such as refactorings. We describe our approach in detail, and demonstrate how it can be used to perform fine-grained software evolution analysis.