Aspect] is a simple and practical aspect-oriented extension to Java With just a few new constructs, AspectJ provides support for modular implementation of a range of crosscutting concerns. In AspectJ's dynamic join point model, join points are well-defined points in the execution of the program; pointcuts are collections of join points; advice are special method-like constructs that can be attached to pointcuts; and aspects are modular units of crosscutting implementation, comprising pointcuts, advice, and ordinary Java member declarations. AspectJ code is compiled into standard Java bytecode. Simple extensions to existing Java development environments make it possible to browse the crosscutting structure of aspects in the same kind of way as one browses the inheritance structure of classes. Several examples show that AspectJ is powerful, and that programs written using it are easy to understand.

An overview of AspectJ

LNBIP reports state-of-the-art results in areas related to business information systems and industrial application software development – timely, at a high level, and in both printed and electronic form.  The type of material published includes  • Proceedings (published in time for the respective event) • Postproceedings (consisting of thoroughly revised and/or extended final papers) • Other edited monographs (such as, for example, project reports or invited volumes) • Tutorials (coherently integrated collections of lectures given at advanced courses, seminars, schools, etc.) • Award-winning or exceptional theses LNBIP is abstracted/indexed in DBLP, EI and Scopus. LNBIP volumes are also submitted for the inclusion in ISI Proceedings.

Lecture notes in business information processing

Employing lightweight, tool-based code review of code changes (aka modern code review) has become the norm for a wide variety of open-source and industrial systems. In this paper, we make an exploratory investigation of modern code review at Google. Google introduced code review early on and evolved it over the years; our study sheds light on why Google introduced this practice and analyzes its current status, after the process has been refined through decades of code changes and millions of code reviews. By means of 12 interviews, a survey with 44 respondents, and the analysis of review logs for 9 million reviewed changes, we investigate motivations behind code review at Google, current practices, and developers' satisfaction and challenges.

https://www.zora.uzh.ch/id/eprint/152982/1/icse2018seip.pdf

Modern code review: a case study at google

With the advent of large code repositories and sophisticated search capabilities, code search is increasingly becoming a key software development activity. In this work we shed some light into how developers search for code through a case study performed at Google, using a combination of survey and log-analysis methodologies. Our study provides insights into what developers are doing and trying to learn when per- forming a search, search scope, query properties, and what a search session under different contexts usually entails. Our results indicate that programmers search for code very frequently, conducting an average of five search sessions with 12 total queries each workday. The search queries are often targeted at a particular code location and programmers are typically looking for code with which they are somewhat familiar. Further, programmers are generally seeking answers to questions about how to use an API, what code does, why something is failing, or where code is located.

/pdf/how-developers-search-for-code-a-case-study-36kiliw6ld.pdf

How developers search for code: a case study

For a static analysis project to succeed, developers must feel they benefit from and enjoy using it.

https://dl.acm.org/doi/pdf/10.1145/3188720

Lessons from building static analysis tools at Google

https://fileadmin.cs.lth.se/sde/publications/papers/2012-Soderberg-SCP-PrePrint.pdf

Extensible intraprocedural flow analysis at the abstract syntax tree level

Author's version of the work published in: ACM Transactions on Programming Languages and Systems (TOPLAS), 34 (4), 2012; doi:10.1145/2400676.2400678. Integrated development environments (IDEs) increase programmer productivity, providing rapid, interactive feedback based on the syntax and semantics of a language. Unlike conventional parsing algorithms, scannerless generalized-LR parsing supports the full set of context-free grammars, which is closed under composition, and hence can parse languages composed from separate grammar modules. To apply this algorithm in an interactive environment, this paper introduces a novel error recovery mechanism. Our approach is language-independent, and relies on automatic derivation of recovery rules from grammars. By taking layout information into consideration it can efficiently suggest natural recovery suggestions.

Natural and Flexible Error Recovery for Generated Modular Language Environments

A semantic editor, providing services like completion and code browsing, can help users to quickly develop high-quality source code. However, a lot of languages still lack semantic editor support due to the difficulty and costs of development. Tool generation and reuse can greatly alleviate this development task. Specifically, tool generation from a formal specification, such as reference attribute grammars (RAGs), can increase development speed by reusing existing specifications. In this tool demonstration we demonstrate how semantic editors can be built with the aid of JastAdd, a meta-compilation tool based on RAGs. We demonstrate two editors built this way. One for a small object-oriented language, PicoJava, and one for the JastAdd specification language itself.

Building semantic editors using JastAdd: tool demonstration

Integrated Development Environments (IDEs) increase programmer productivity, providing rapid, interactive feedback based on the syntax and semantics of a language. Unlike conventional parsing algorithms, scannerless generalized-LR parsing supports the full set of context-free grammars, which is closed under composition, and hence can parse languages composed from separate grammar modules. To apply this algorithm in an interactive environment, this article introduces a novel error recovery mechanism. Our approach is language independent, and relies on automatic derivation of recovery rules from grammars. By taking layout information into consideration it can efficiently suggest natural recovery suggestions.

/pdf/natural-and-flexible-error-recovery-for-generated-modular-3pnd5cry49.pdf

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.

Emma Söderberg

Author Tools

Chat about Author

Papers

Modern code review: a case study at google

Extensible intraprocedural flow analysis at the abstract syntax tree level

Natural and Flexible Error Recovery for Generated Modular Language Environments

Building semantic editors using JastAdd: tool demonstration

Natural and Flexible Error Recovery for Generated Modular Language Environments