At the heart of software evolution is a sequence of edit actions, called an edit script, made to a source code file. Since software systems are stored version by version, the edit script has to be computed from these versions, which is known as a complex task. Existing approaches usually compute edit scripts at the text granularity with only add line and delete line actions. However, inferring syntactic changes from such an edit script is hard. Since moving code is a frequent action performed when editing code, it should also be taken into account. In this paper, we tackle these issues by introducing an algorithm computing edit scripts at the abstract syntax tree granularity including move actions. Our objective is to compute edit scripts that are short and close to the original developer intent. Our algorithm is implemented in a freely-available and extensible tool that has been intensively validated.

/pdf/fine-grained-and-accurate-source-code-differencing-fqzra1km4q.pdf

Fine-grained and accurate source code differencing

Third-party library reuse has become common practice in contemporary software development, as it includes several benefits for developers. Library dependencies are constantly evolving, with newly added features and patches that fix bugs in older versions. To take full advantage of third-party reuse, developers should always keep up to date with the latest versions of their library dependencies. In this paper, we investigate the extent of which developers update their library dependencies. Specifically, we conducted an empirical study on library migration that covers over 4,600 GitHub software projects and 2,700 library dependencies. Results show that although many of these systems rely heavily on dependencies, 81.5% of the studied systems still keep their outdated dependencies. In the case of updating a vulnerable dependency, the study reveals that affected developers are not likely to respond to a security advisory. Surveying these developers, we find that 69% of the interviewees claimed to be unaware of their vulnerable dependencies. Moreover, developers are not likely to prioritize a library update, as it is perceived to be extra workload and responsibility. This study concludes that even though third-party reuse is common practice, updating a dependency is not as common for many developers.

Do developers update their library dependencies

Third-party library reuse has become common practice in contemporary software development, as it includes several benefits for developers. Library dependencies are constantly evolving, with newly added features and patches that fix bugs in older versions. To take full advantage of third-party reuse, developers should always keep up to date with the latest versions of their library dependencies. In this paper, we investigate the extent of which developers update their library dependencies. Specifically, we conducted an empirical study on library migration that covers over 4,600 GitHub software projects and 2,700 library dependencies. Results show that although many of these systems rely heavily on dependencies, 81.5% of the studied systems still keep their outdated dependencies. In the case of updating a vulnerable dependency, the study reveals that affected developers are not likely to respond to a security advisory. Surveying these developers, we find that 69% of the interviewees claim that they were unaware of their vulnerable dependencies. Furthermore, developers are not likely to prioritize library updates, citing it as extra effort and added responsibility. This study concludes that even though third-party reuse is commonplace, the practice of updating a dependency is not as common for many developers.

Do Developers Update Their Library Dependencies? An Empirical Study on the Impact of Security Advisories on Library Migration

Source code metrics

Software developers interact with APIs on a daily basis and, therefore, often face the need to learn how to use new APIs suitable for their purposes. Previous work has shown that recommending usage patterns to developers facilitates the learning process. Current approaches to usage pattern recommendation, however, still suffer from high redundancy and poor run-time performance. In this paper, we reformulate the problem of usage pattern recommendation in terms of a collaborative-filtering recommender system. We present a new tool, FOCUS, which mines open-source project repositories to recommend API method invocations and usage patterns by analyzing how APIs are used in projects similar to the current project. We evaluate FOCUS on a large number of Java projects extracted from GitHub and Maven Central and find that it outperforms the state-of-the-art approach PAM with regards to success rate, accuracy, and execution time. Results indicate the suitability of context-aware collaborative-filtering recommender systems to provide API usage patterns.

https://hal.archives-ouvertes.fr/hal-02023023/document

FOCUS: a recommender system for mining API function calls and usage patterns

Software systems intensively depend on external libraries, chosen at conception time. However, relevance of any library irremediably changes during projects and/or library life cycle. As a consequence, projects developers must periodically reconsider the libraries they depend on, and must think about library migration. When they want to migrate their libraries, they then have to identify candidate libraries that offer similar facilities and thus can substitute to each other. They also have to compare candidates to choose the one that best fits their needs. Finding a relevant library replacement is a well known tedious and time-consuming task. In this paper, we propose an approach that identifies sets of similar libraries and that produces what we call library migration graphs that show how existing projects have performed migrations among them. These graphs, constructed from the observation of a large number of software projects, ease the discovery and selection of library replacements.

/pdf/mining-library-migration-graphs-2fguq49tip.pdf

Mining Library Migration Graphs

Library migration is the process of replacing a third-party library in favor of a competing one during software maintenance. The process of transforming a software source code to become compliant with a new library is cumbersome and error-prone. Indeed, developers have to understand a new Application Programming Interface (API) and search for the right replacements for the functions they use from the old library. As the two libraries are independent, the functions may have totally different structures and names, making the search of mappings very difficult. To assist the developers in this difficult task, we introduce an approach that analyzes source code changes from software projects that already underwent a given library migration to extract mappings between functions. We demonstrate the applicability of our approach on several library migrations performed on the Java open source software projects.

Automatic discovery of function mappings between similar libraries

Context: Expert identification is becoming critical to ease the communication between developers in case of global software development or to better know members of large software communities. To quickly identify who are the experts that will best perform a given development task, both the assignment of skills to developers and the computation of their corresponding expertise level have to be automated. Since the real level of expertise is tedious to assess, our challenge is to identify developers having a significant level of experience with respect to a skill. Method: In this paper we propose XTic, an approach that takes up this challenge with the intent to be accurate and efficient. XTic provides a language to specify skills. It also provides an automatic process that extracts skills and experience levels from source code repositories. Our approach is based on the idea that an expert has a high level of experience with respect to a skill. Results: We have validated XTic both on open source and industrial projects to measure its accuracy and its efficiency. The results we obtained show that its accuracy is between moderate and strong and that it scales well with medium and large size software projects. Conclusion: XTic supports the specification of a diversity of developer skills and the extraction of the expertise of these developers under the form of level of experience.

/pdf/automatic-extraction-of-developer-expertise-2ojoz6sjlo.pdf

Automatic extraction of developer expertise

Heavy usage of third-party libraries is almost mandatory in modern software systems. The knowledge of these libraries is generally scattered across the development team. When a development or a maintenance task involving specific libraries arises, finding the relevant experts would simplify its completion. However there is no automatic approach to identify these experts. In this article we propose Libtic, a search engine of library experts automatically populated by mining software repositories. We show that Libtic finds relevant experts of common Java libraries among the GitHub developers. We also illustrate its usefulness through a case study on the Apache HBase project where several maintenance and development use-cases are carried out.

Find your library experts

Ensuring models' consistency is a key concern when using a model-based development approach. Therefore, model inconsistency detection has received significant attention over the last years. To be useful, inconsistency detection has to be sound, efficient, and scalable. Incremental detection is one way to achieve efficiency in the presence of large models. In most of the existing approaches, incrementalization is carried out at the expense of the memory consumption that becomes proportional to the model size and the number of consistency rules. In this paper, we propose a new incremental inconsistency detection approach that only consumes a small and model size-independent amount of memory. It will therefore scale better to projects using large models and many consistency rules. Copyright © 2012 John Wiley & Sons, Ltd.

https://hal.archives-ouvertes.fr/hal-00975337/document

Incremental inconsistency detection with low memory overhead

Handle everyday research tasks with reliable, citation-backed results

Your personal Research Agent to handle research tasks with citation-backed results

Popular Tasks used by Researchers

How can I help with your research?

Meet SciSpace

Get more enhanced response by uploading the PDFs you want me to reference.

No relevant PDFs in your library

SciSpace is the AI research assistant for academics. Run systematic literature reviews on 280M+ papers, and write papers with cited sources. Trusted by 1M+ students, PhDs & researchers.

SciSpace | AI for Research

Analyze PDFs

Code & Manuscripts

Funding & Grants

Literature & Patents

Medical & Clinical Data

Systematic Review

Visualize & Present

Web & Data

Build a Google Scholar-like website for your research.

Build a website

Create charts and images for your research

Create a Chart

Write a paper for submission to a journal

Draft a manuscript

Patent Search

Design eye-catching scientific posters in minutes.

Scientific Poster Generation

Systematic Literature Review

One task is running at the moment. Your messages will be shown right after.

Drag and drop or click here to browse

Loved by <highlight>1 million+</highlight> researchers

Extract a list of specific topics and their sources from unstructured text

Topics

Compare and analyze relevant papers that matches with your search

Papers

Get insights from PDFs and bookmarked papers from your library

My library

Recent searches

Try searching for:

Catch AI-generated content in scholarly and non-scholarly content

{ai} Detector

Ai Writer

Get PDF Summaries, highlighted text explanations 

Chat with PDF

Effortlessly create in-text citations and bibliographies in APA and 2,500 other formats

Citation generator

Get explanations, summaries, and answers on academic papers

Ease up your research workflow with {scispace}'s cohort of exciting AI tools

Elevate your academic writing skills and convey your ideas the way you want

Paraphraser

Explore our range of reading and writing tools

Your file is being prepared and should be ready in a few minutes. If it's a large file, it might take a bit longer. You can close this window, and we'll email you the file when it's done.

You have reached a maximum limit of <strong>{limit}</strong> columns in the table. Remove at least <strong>1</strong> column to add or create another one.

Cédric Teyton

Author Tools

Chat about Author