Luigi Cerulo

How Clones are Maintained: An Empirical Study

Despite the conventional wisdom concerning the risks related to the use of source code cloning as a software development strategy, several studies appeared in literature indicated that this is not true. In most cases clones are properly maintained and, when this does not happen, is because cloned code evolves independently. Stemming from previous works, this paper combines clone detection and co-change analysis to investigate how clones are maintained when an evolution activity or a bug fixing impact a source code fragment belonging to a clone class. The two case studies reported confirm that, either for bug fixing or for evolution purposes, most of the cloned code is consistently maintained during the same co-change or during temporally close co-changes

Impact analysis by mining software and change request repositories

Impact analysis is the identification of the work products affected by a proposed change request, either a bug fix or a new feature request. In many open-source projects, such as KDE, Gnome, Mozilla, Openoffice, change requests, and related data, are stored in a bug tracking system such as Bugzilla. These data, together with the data stored in a versioning system, such as CVS, are a valuable source of information on which useful analyses can be performed. In this paper we propose a method to derive the set of source files impacted by a proposed change request. The method exploits information retrieval algorithms to link the change request description and the set of historical source file revisions impacted by similar past change requests. The method is evaluated by applying it on four open-source projects

Supporting change request assignment in open source development

Software repositories, such as CVS and Bugzilla, provide a huge amount of data regarding, respectively, source code and change request history. In this paper we propose a study on how change requests have been assigned to developers involved in an open source project and a method to suggest the set of best candidate developers to resolve a new change request. The method is based on the hypothesis that, given a new change request, developers that have resolved similar change requests in the past are the best candidates to resolve the new one. The suggestion can be useful for project managers in order to choose the best candidate to resolve a particular change request and/or to construct a competence database of developers working on software projects. We use the textual description of change requests stored in software repositories to index developers as documents in an information retrieval system. An Information Retrieval method is then applied to retrieve the candidate developers using the textual description of a new change request as a query.Case and evaluation study of the analysis and the methods introduced in this paper has been conducted on two large open source projects, Mozilla and KDE.

Identifying Changed Source Code Lines from Version Repositories

Observing the evolution of software systems at different levels of granularity has been a key issue for a number of studies, aiming at predicting defects or at studying certain phenomena, such as the presence of clones or of crosscutting concerns. Versioning systems such as CVS and SVN, however, only provide information about lines added or deleted by a contributor: any change is shown as a sequence of additions and deletions. This provides an erroneous estimate of the amount of code changed. This paper shows how the evolution of changes at source code line level can be inferred from CVS repositories, by combining information retrieval techniques and the Levenshtein edit distance. The application of the proposed approach to the ArgoUML case study indicates a high precision and recall.

An empirical study on the evolution of design patterns

Design patterns are solutions to recurring design problems, conceived to increase benefits in terms of reuse, code quality and, above all, maintainability and resilience to changes. This paper presents results from an empirical study aimed at understanding the evolution of design patterns in three open source systems, namely JHotDraw, ArgoUML, and Eclipse-JDT. Specifically, the study analyzes how frequently patterns are modified, to what changes they undergo and what classes co-change with the patterns. Results show how patterns more suited to support the application purpose tend to change more frequently, and that different kind of changes have a different impact on co-changed classes and a different capability of making the system resilient to changes.

Fine grained indexing of software repositories to support impact analysis

Versioned and bug-tracked software systems provide a huge amount of historical data regarding source code changes and issues management. In this paper we deal with impact analysis of a change request and show that data stored in software repositories are a good descriptor on how past change requests have been resolved. A fine grained analysis method of software repositories is used to index code at different levels of granularity, such as lines of code and source files, with free text contained in software repositories. The method exploits information retrieval algorithms to link the change request description and code entities impacted by similar past change requests. We evaluate such approach on a set of three open-source projects.

Achievements and challenges in software reverse engineering

Deeply understanding the intricacies of software must always come before any considerations for modifying it.

Learning gene regulatory networks from only positive and unlabeled data

BackgroundRecently, supervised learning methods have been exploited to reconstruct gene regulatory networks from gene expression data. The reconstruction of a network is modeled as a binary classification problem for each pair of genes. A statistical classifier is trained to recognize the relationships between the activation profiles of gene pairs. This approach has been proven to outperform previous unsupervised methods. However, the supervised approach raises open questions. In particular, although known regulatory connections can safely be assumed to be positive training examples, obtaining negative examples is not straightforward, because definite knowledge is typically not available that a given pair of genes do not interact.ResultsA recent advance in research on data mining is a method capable of learning a classifier from only positive and unlabeled examples, that does not need labeled negative examples. Applied to the reconstruction of gene regulatory networks, we show that this method significantly outperforms the current state of the art of machine learning methods. We assess the new method using both simulated and experimental data, and obtain major performance improvement.ConclusionsCompared to unsupervised methods for gene network inference, supervised methods are potentially more accurate, but for training they need a complete set of known regulatory connections. A supervised method that can be trained using only positive and unlabeled data, as presented in this paper, is especially beneficial for the task of inferring gene regulatory networks, because only an incomplete set of known regulatory connections is available in public databases such as RegulonDB, TRRD, KEGG, Transfac, and IPA.

An empirical study of the relationships between design pattern roles and class change proneness

Analyzing the change-proneness of design patterns and the kinds of changes occurring to classes playing role(s) in some design pattern(s) during software evolution poses the basis for guidelines to help developers who have to choose, apply or maintain design patterns. Building on previous work, this paper shifts the focus from design patterns as wholes to the finer-grain level of design pattern roles. The paper presents an empirical study to understand whether there are roles that are more change-prone than others and whether there are changes that are more likely to occur to certain roles. The study relies on data extracted from the source code repositories of three different systems (JHotDraw, Xerces, and Eclipse-JDT) and from 12 design patterns. Results obtained confirm the intuitive behavior about changeability of many roles in design motifs, but also warns about properly designing parts of the motif subject to frequent changes.

Using multivariate time series and association rules to detect logical change coupling: An empirical study

In recent years, techniques based on association rules discovery have been extensively used to determine change-coupling relations between artifacts that often changed together. Although association rules worked well in many cases, they fail to capture logical coupling relations between artifacts modified in subsequent change sets.