#Mucommander source code manual#
#Mucommander source code software#
#Mucommander source code code#

Our solution approach, grounded on surveys of industry practice as well as scientific literature, is to support navigation by combining information retrieval and machine learning into Recommendation Systems for Software Engineering (RSSE). While IA is fundamental in all large software projects, CIA is particularly important to safety-critical development.

#Mucommander source code code#

IA is the early task of allocating an issue report to a development team, and CIA is the subsequent activity of identifying how source code changes affect the existing software artifacts. In this thesis, we address two tasks involved in issue management: Issue Assignment (IA) and Change Impact Analysis (CIA). Efficient management of incoming issue reports requires the successful navigation of the information landscape of a project. As software systems often evolve over many years, a large number of issue reports is typically managed during the lifetime of a system, representing the units of work needed for its improvement, e.g., defects to fix, requested features, or missing documentation. Software developers in large projects work in complex information landscapes and staying on top of all relevant software artifacts is an acknowledged challenge. Comparison with the state-of-the-art techniques and their variants report that our technique can improve 19% in and 20% in over the state-of-the-art, and can improve 59% of the noisy queries and 39% of the poor queries. Experiments using 5,139 bug reports show that our technique can localize the buggy source documents with 7% - 56% higher 6% - 62% higher and 6% - 62% higher than the baseline technique.

In particular, our technique determines whether there are excessive program entities or not in a bug report (query), and then applies appropriate reformulations to the query for bug localization. In this paper, we propose a novel technique - BLIZZARD - that automatically localizes buggy entities from project source using appropriate query reformulation and effective information retrieval. Conversely, excessive structured information (e.g., stack traces) in the bug report might not always help the automated localization either. Recent findings suggest that Information Retrieval (IR)-based bug localization techniques do not perform well if the bug report lacks rich structured information (e.g., relevant program entity names).

These results show the applicability of our approach to software projects without history. Over the projects analysed, on average we find one or more affected files in the top 10 ranked files for 76% of the bug reports. Out of 30 performance indicators, we improve 27 and equal 2. We compare our approach to eight others, using their own five metrics on their own six open source projects.

#Mucommander source code manual#

The scoring method is based on heuristics identified through manual inspection of a small sample of bug reports. We present a novel approach that directly scores each current file against the given report, thus not requiring past code and reports. However, current state-of-the-art IR approaches rely on project history, in particular previously fixed bugs or previous versions of the source code. Such approaches have the advantage of not requiring expensive static or dynamic analysis of the code. via a bug report, where is it located in the source code? Information retrieval (IR) approaches see the bug report as the query, and the source code files as the documents to be retrieved, ranked by relevance. Yes, they startup slowly, but at least they provided meaningful and fast autocomplete without the need to wait for the language sever or VScode to unfreeze.Īgain these are purely anecdote, but please don't purely judge an application by it's startup time if it saves you time in the end.Bug localisation is a core program comprehension task in software maintenance: given the observation of a bug, e.g. DBeaver has an absolutely massive feature set tailored for database work and it has to load all those plugins at some point.Īnother counter point is VSCode with java-lsp could never handle the 300k loc code base we developed on, where intelij and eclipse do just fine. By the time that work is done it could be equally slow to start up.

Could I do the same kind of work in VSCode? Absolutely not, unless I wanted to build out an entirely new interface on top of VSCode. Also you are comparing two very different types of applications.Ī text editor that can be extended vs a platform application is the same argument we've been having about vim vs emacs for years.ĭBeaver is an incredibly powerful application that had allowed me to work in Postgres, Teradata, and Oracle databases for years. Start time is not a great measure of overall performance.