اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدFQL: An Extensible Feature Query Language and Toolkit on Searching Software Characteristics for HPC Applications
443
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
The amount of large-scale scientific computing software is dramatically increasing. In this work, we designed a new language, named feature query language (FQL), to collect and extract software features from a quick static code analysis. We designed and implemented an FQL toolkit to automatically detect and present the software features using an extensible query repository. Several large-scale, high performance computing (HPC) scientific codes have been used in the paper to demonstrate the HPC-related feature extraction and information collection. Although we emphasized the HPC features in the study, the toolkit can be easily extended to answer general software feature questions, such as coding pattern and hardware dependency.
قيم البحث
اقرأ أيضاً
XML stands for the Extensible Markup Language. It is a markup language for documents, Nowadays XML is a tool to develop and likely to become a much more common tool for sharing data and store. XML can communicate structured information to other users
. In other words, if a group of users agree to implement the same kinds of tags to describe a certain kind of information, XML applications can assist these users in communicating their information in an more robust and efficient manner. XML can make it easier to exchange information between cooperating entities. In this paper we will present the XML technique by fourth factors Strength of XML, XML Parser, XML Goals and Types of XML Parsers.
This note concerns a search for publications in which the pragmatic concept of a test as conducted in the practice of software testing is formalized, a theory about software testing based on such a formalization is presented or it is demonstrated on
the basis of such a theory that there are solid grounds to test software in cases where in principle other forms of analysis could be used. This note reports on the way in which the search has been carried out and the main outcomes of the search. The message of the note is that the fundamentals of software testing are not yet complete in some respects.
Performing dependability evaluation along with other analyses at architectural level allows both making architectural tradeoffs and predicting the effects of architectural decisions on the dependability of an application. This paper gives guidelines
for building architectural dependability models for software systems using the AADL (Architecture Analysis and Design Language). It presents reusable modeling patterns for fault-tolerant applications and shows how the presented patterns can be used in the context of a subsystem of a real-life application.
Software engineering bots - automated tools that handle tedious tasks - are increasingly used by industrial and open source projects to improve developer productivity. Current research in this area is held back by a lack of consensus of what software
engineering bots (DevBots) actually are, what characteristics distinguish them from other tools, and what benefits and challenges are associated with DevBot usage. In this paper we report on a mixed-method empirical study of DevBot usage in industrial practice. We report on findings from interviewing 21 and surveying a total of 111 developers. We identify three different personas among DevBot users (focusing on autonomy, chat interfaces, and smartness), each with different definitions of what a DevBot is, why developers use them, and what they struggle with. We conclude that future DevBot research should situate their work within our framework, to clearly identify what type of bot the work targets, and what advantages practitioners can expect. Further, we find that there currently is a lack of general purpose smart bots that go beyond simple automation tools or chat interfaces. This is problematic, as we have seen that such bots, if available, can have a transformative effect on the projects that use them.
Software design patterns are standard solutions to common problems in software design and architecture. Knowing that a particular module implements a design pattern is a shortcut to design comprehension. Manually detecting design patterns is a time c
onsuming and challenging task; therefore, researchers have proposed automatic design patterns detection techniques to facilitate software developers. However, these techniques show low performance for certain design patterns. In this work, we introduce an approach that improves the performance over the state-of-the-art by using code features with machine learning classifiers to automatically train a design pattern detection. We create a semantic representation of source code from the code features and the call graph, and apply the Word2Vec algorithm on the semantic representation to construct the word-space geometric model of the Java source code. DPD_F then uses a Machine Learning approach trained using the word-space model and identifies software design patterns with 74% Precision and 71% Recall. Additionally, we have compared our results with two existing design pattern detection approaches namely FeatureMaps & MARPLE-DPD. Empirical results demonstrate that our approach outperforms the benchmark approaches by 30% and 10% respectively in terms of Precision. The runtime performance also supports its practical applicability.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
