Software Engineering Research Paper Summery Automatically Documenting Program Changes

Note: This is only summery

Automatically Documenting Program Changes

What are motivations for this work?

Log messages are mostly with source code. These messages are important component of software maintenance. The coder can get some help by understanding editing, point and triage the defects. The technical problem is that this log documentation is burden to create and it may be partially complete or inaccurate.

What is the work's evaluation of the proposed solution?

We introduce an automatic technique for manufacturing concise, human readable documentation for arbitrary program differences. For code summarization, our algorithm is based along the combination of symbolic execution and novel base approach. The papers produced by algorithm describes the result of a change on the run time behavior of the program, it also includes conditions under which program behavior changes and what the new behavior is.  

What is your analysis of the identified problem, idea and evaluation?

Mostly developer spend their most of the time trying to read code. I guess it is good algorithm that describe the consequence of a change of behavior of the program. We discover that our generated documentation is suitable for replacing of existing log messages that directly identify a code modification.    

 What are the contributions? 

The principal one of the contribution is an empirical, mathematical study of the use of the version control log messages in many open source software organizations. Work shows us there are many messages that that comprised with what and why documentation also find that use is commonplace. An algorithm (DeltaDoc) is utilized for identifying the varieties and condition under which they are occurring, combined with a set of conversion heuristics the change summarization. By combing, these techniques automatically generate a human readable description of code modifications.

For objectively quantifying and comparing the data capacity of program documentation a novel process is applied. We try out this algorithm on a paradigm and a conflict of its yield to 250 human written messages from five projects. Our experiments supported by a human study, which suggest DeltaDoc could replace over 89 percent of human code generated what log messages.  

 What are future directions for this research? 

In future we can enhance our DeltaDoc program efficiency by adding more techniques, adding more projects, doing more brief experiments. We can increase the human written log messages and by applying efficient algorithm the productivity is also increased. Including the condition under which the program behavior changes and what the new behavior is.

What questions are you left with? 

I guess the main inquiry is that, is this algorithm operates on a distributed network system with wide date. Is the error percentage is more serious with another system and documentation errors are minimized by adding some fresh techniques and experiments.

 What is your take-away message from this paper?

We purpose a DeltaDoc, an algorithm for fetching human readable code. Our technique is made up with symbolic execution and a novel base approach to code summarization. It states us what a code change affects.  Our documentation describes the result of modification of conduct of a program and what the new conduct was.

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Software Engineering Research Paper Summery A UML-Based Approach to System Testing

Note : This is only own words summery of above paper

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Along the base of specification system testing is concerned with testing an integral arrangement. In OOP, UML development means that system test requirements are derived from UML analysis artifacts such as use cases, their conforming sequence and collaboration diagrams, class diagrams. We are working to hold up the derivation of functional system test requirements, that all changed test cases, test driver once we have detailed design data. We discussed testability and automation issues, as the final goal is to support system testing activities with the high skill tool. TOTEM (Testing Object-orienTed systems with the unified Modeling language) functional test methodology, drive test requirements  from early artifacts produced at the final stage of analysis stage.

Authors: Briand, Lionel and Labiche, Yvan

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Software Engineering Research Paper Summery Systematic Review of Automatic Test Case Generation by UML Diagrams

Note: That sentences are only own words summery of original paper

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The most significant activity in SDLC is software testing. By time passing the monetary value of manual test cases and also steadfastness of the software researchers and followers have projected many automated test generation techniques but still have a work. This study showed a systematic view of the study performed in the area of automated test generation of test case, particularly related to UML based automated test case generation. The aim of this survey was to collect an adequate data to apprehend and gain deeper visions into the nature of the various testing techniques available and possibility of the best. Foremost we need to get the existing technique that, what were they were getting along already. Thither are a great number of techniques for test generation usecase. Hence a deeper concentration on the existing techniques required. We require to better take in those techniques, their differences and explore novel methods to explore them well. The new methods need will arise from driven study. Unified modeling language has at once become a de facto standard in the area of software testing. New techniques for test case generation from UML needs to be researched. 

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 Authors:  Kaur, Arvinder and Vig, Vidhi

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Software Engineering Research Paper Summery A TEST GENERATION METHOD BASED ON STATE DIAGRAM

Note : This is only summery of above paper

by : NICHA KOSINDRDECHA, 2JIRAPUN DAENGDEJ

In generally software testing phase takes 40-70% of the fourth dimension and cost during the software development life cycle. Thither are many researchers who found many test case generation methods to lessen the price and time, there are still a number of important research publication. The motivation for this study is to chomp a great quantity of time and cost to automatically generate tests from the diagram, with a large size of test and less test code coverage. We presented an effective test sequence generation technique to minimize time, cost, size of the tests while maximizing test coverage. Our anticipated technique target to see and generate tests from the state chart diagram. The diagram mostly used to excuse the conduct of the system, also discuss and determine the best effective test generation methods that derive tests from the diagram. Concluding that existing techniques since 1990, this paper introduces a new “3S” classification of test case generation techniques, which are: specification base technique, sketch diagram base technique and source based technique. Also, this paper's purpose a new “2S” classification of existing test data generation as follows: specification based technique and source code based technique. Specification base approach used input and production with pre-condition that is generated from requirement specification. The source code based technique aims to design test data using control flow graph and source code as well. As a result this work found that TGFMMD method is the best to produce the smaller size of test cases, with minimum total time and hatch all 100% nodes in the state chart diagram

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Software Engineering Static Analysis Implementation in Automated test Generation its code flowchart

Static Analysis 

code step 1 : suppose we have code like this 

int twic (int v) {

return 2*v;

}

void testme (int x  int y) {

z= twic(y);

if (z==x) {

if(x>y+10) {

error}

}

}

Step 2 : Make a flow graph of the program 

Step 3: Write them into mathematically form

after step 2 you have to write your diagram into math form like

suppose  sign like your code

$(sigma type sign) : {x->xo  y->yo}       path condition Pc : true

if yes    $ : {x->xo  y->yo   z->2*yo}                   (pc : true)^(2*yo=xo)

if false  $ : {x->xo  y->yo   z->2*yo}                   (pc : true)^(2*yo!=xo)

Inner loop if True

$ : {x->xo  y->yo   z->2*yo}                   (pc : true)^(2*yo=xo)^xo>yo+10

Inner loop if False

$ : {x->xo  y->yo   z->2*yo}                   (pc : true)^(2*yo=xo)^xo<=yo+10

at the end valuse after solution 

xo=2

yo=1

and hence .

Contact Me for any help 

cheers !

              

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