scholarly journals Verification of Atomicity Preservation in Model-to-Code Transformations using Generic Java Code

2016 ◽  
Author(s):  
Dan Zhang ◽  
Dragan Bosnacki ◽  
Mark van den Brand ◽  
Cornelis Huizing ◽  
Ruurd Kuiper ◽  
...  
Keyword(s):  
Code Transformations ◽  
Java Code

Selenium Broken Links Automation Testing

2017 ◽  
Vol 7 (7) ◽  
pp. 53
Author(s):  
Gursimran Singh ◽  
Harpreet Kaur
Keyword(s):  
Web Page ◽  
Automate Testing ◽  
Website Content ◽  
Java Code ◽  
Automation Testing

With the growth of website content it is become difficult to manage relations between Individual webpage and keep track of their hyperlinks within a website. This causes some Hyperlink become dead or broken. A broken Link  is a  link on a web page that no longer works. It is difficult to find out the broken link manually by checking each hyperlink individually because it is time consuming and tedious work. So to eliminate this we can use the selenium web driver tool and java code to automate testing of each hyperlink individually. The objective of this thesis is to automate finding of broken links using selenium web driver tool.

scholarly journals Characterising the Knowledge about Primitive Variables in Java Code Comments

2021 ◽  
Author(s):  
Mahfouth Alghamdi ◽  
Shinpei Hayashi ◽  
Takashi Kobayashi ◽  
Christoph Treude
Keyword(s):  
Java Code ◽  
Primitive Variables

A TRANSLATOR OF SOFTWARE ARCHITECTURE DESIGN FROM SAM TO JAVA

2007 ◽  
Vol 17 (06) ◽  
pp. 709-755 ◽  
Author(s):  
YUJIAN FU ◽  
ZHIJIANG DONG ◽  
XUDONG HE
Keyword(s):  
Petri Nets ◽  
Software Architecture ◽  
Temporal Logic ◽  
Subsequent Development ◽  
Experimental Result ◽  
Architecture Design ◽  
Architecture Model ◽  
Architecture Description ◽  
Java Code ◽  
Software Architecture Design

A software architecture design has many benefits including aiding comprehension, supporting early analysis, and providing guidance for subsequent development activities. An additional major benefit is if a partial prototype implementation can be automatically generated from a given software architecture design. However, in the past decade less progress was made on automatically realizing software architecture designs. In this paper, we present a translator for automatically generating an implementation from a software architectural description. The implementation not only captures the functionality of the given architecture description, but also contains additional monitoring code for ensuring desirable behavior properties through runtime verification. Our method takes a software description written in SAM, a software architecture model integrating dual formal methods Petri nets and temporal logic, and generates ArchJava/Java/AspectJ code. More specifically, the structure of a SAM architecture description produces ArchJava code, the behavior models of components/connectors represented in Petri nets lead to plain Java code, and the property specifications defined in temporal logic generate AspectJ code; the above code segments are then integrated into Java code. An experimental result is provided.

Nobrainer: An Example-Driven Framework for C/C++ Code Transformations

2019 ◽  
pp. 140-155
Author(s):  
Valeriy Savchenko ◽  
Konstantin Sorokin ◽  
Georgiy Pankratenko ◽  
Sergey Markov ◽  
Alexander Spiridonov ◽  
...  
Keyword(s):  
Code Transformations

scholarly journals Randomized heuristic for the maximum clique problem

2020 ◽  
Author(s):  
Shalin Shah
Keyword(s):  
Approximation Algorithm ◽  
Randomized Algorithm ◽  
Maximum Clique ◽  
Constant Factor ◽  
Maximum Clique Problem ◽  
Hard Problem ◽  
Np Hard ◽  
Np Hard Problem ◽  
Java Code ◽  
Clique Problem

<p>A clique in a graph is a set of vertices that are all directly connected</p><p>to each other i.e. a complete sub-graph. A clique of the largest size is</p><p>called a maximum clique. Finding the maximum clique in a graph is an</p><p>NP-hard problem and it cannot be solved by an approximation algorithm</p><p>that returns a solution within a constant factor of the optimum. In this</p><p>work, we present a simple and very fast randomized algorithm for the</p><p>maximum clique problem. We also provide Java code of the algorithm</p><p>in our git repository. Results show that the algorithm is able to find</p><p>reasonably good solutions to some randomly chosen DIMACS benchmark</p><p>graphs. Rather than aiming for optimality, we aim to find good solutions</p><p>very fast.</p>

scholarly journals Randomized heuristic for the maximum clique problem

2020 ◽  
Author(s):  
Shalin Shah
Keyword(s):  
Approximation Algorithm ◽  
Randomized Algorithm ◽  
Maximum Clique ◽  
Constant Factor ◽  
Maximum Clique Problem ◽  
Hard Problem ◽  
Np Hard ◽  
Np Hard Problem ◽  
Java Code ◽  
Clique Problem

<p>A clique in a graph is a set of vertices that are all directly connected</p><p>to each other i.e. a complete sub-graph. A clique of the largest size is</p><p>called a maximum clique. Finding the maximum clique in a graph is an</p><p>NP-hard problem and it cannot be solved by an approximation algorithm</p><p>that returns a solution within a constant factor of the optimum. In this</p><p>work, we present a simple and very fast randomized algorithm for the</p><p>maximum clique problem. We also provide Java code of the algorithm</p><p>in our git repository. Results show that the algorithm is able to find</p><p>reasonably good solutions to some randomly chosen DIMACS benchmark</p><p>graphs. Rather than aiming for optimality, we aim to find good solutions</p><p>very fast.</p>

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