How to write Research Purposal its flow chart

Typically a research purposal consist of these parts

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How to do Software Engineering Research work ?

There are many steps, that you can follow to adopt the successful software Engineering Research and Its Advantages

Research is usually focused on  

>  solving a problem

> or addressing an issue

> or answering a question 

How to do Research 

> Select a specific topic in a research domain

> Identify problem, issue or questions with the help of people, who are related to your study and needs a solution

Identify sources you can use to get your questions answered, basic sources include 

>  The library 

>  The internet

>  People 

> Observations

Once you are clear about the problem, we have to search for what has already been done by

>  Reading latest research paper

>  Listen to experts

>  Talks

>  Check for research groups pages doing same study

- Now we are almost clear what we have to do so we adopt a method

- Planning  the time and cost to execute

- Now compare your results with existing methods 

- Evaluate the differences

- Justify the usefulness of your method  

- Now write it what you did 

- Submit it 

- Now it can be evaluated by experts in that field in order to know the worth 

- Submit it only in well known journals or conferences of you research of work 

Advantages of Publishing Research 

There are many benefits of publishing research paper, and here we discussed some of following 

> To help improve writing and research skills

> To experience the scholarly publication process

> To connect the researcher and professors 

> To inform a future career path 

Summery of topic

- We discussed about research, its implementing steps

- Discussed Research questions and literature review sources

- We also discussed some advantages of publishing a research paper 

References : 1.researchpedia.info

2. http://www.writesite.org/html/howto.htm

3. https://publish.illinois.edu/ugresearch/2014/10/14/the-benefits-of-publishing-as-an-graduate

  

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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 of Combined Static and Dynamic Automated Test Generation for MS and PHD

Summery of Combined Static and Dynamic Automated Test Generation for MS and PHD

Area: Software System Quality Assurance

By Sai Zhang, David Saff, Yingyi Bu

The motivation for automated test in today’s fast moving world, there is a challenge for any society to constantly defend and improve the character and efficiency of software system evolution In many software projects, software testing is ignored, there are many factors behind this like cost and time and so on that may result in deficiency of product quality and customer dissatisfaction and in the end to increase the overall software quality cost. Poor test approach, misjudge the effort of test case generation, delay in testing and following test maintenance are the main reason behind for cost mostly.

Mostly A unit test consists of a flow of approaches calls that create and modify objects, then use them as a parameter to a method under test. It is challenging task to automatically generate sequences that are original correct. This paper purposes a combined static and dynamic, automated test generation to address these problems for code without proper specification. Our first tactic uses dynamic analysis to suppose a call sequence from a sample program execution, then we use static analysis to recognize method craving relations based on the subjects they may say and compose. At the end, we combine the both dynamic model and the statically identified dependence information lead a random test generator to create a legal behaviorally test.

There are many several past research tools that follow an approach similar to us, but they neglect the two or three stages of our approach. Randoop, Palul and RecGen are different testing tools that are used in past. Paul presents the dynamic random approaches. RecGen uses a static does not have dynamic form and applies a static analysis to implement random test generation. Randoop is a pure random test generation tool.Compared to old approaches, Palus increases the structural coverage of generating test and improve their ability to detect errors. We applied it on half a dozen popular open source applications. The test results by Palus attained much higher structural coverage and found more unknown bugs than the other overtures.

Palus approach consists of four component names are, a load time instrumentation and dynamic model component, a static method analysis and a guided random test generation component. We present later a detailed introduction of all components

Related work for automated test generation techniques for OOP have been projected in the final decade. Like JCrasher creates test inputs by using a parameter graph to find method calls whose returns values can serve as input parameter. MSeqGen mines client code bases statically and extracts a frequent pattern as implicit programing rules that are practiced to support in generating tests. Another two alternatives approaches to create test input objects are with direct heap handling and using capture reply techniques.

For future work we are concerned with exploring two research directions. Mainly we plan to use different models like ADABU to guide an automated test generation.. Second, we are interested to train machine learning techniques to complement the dynamically inferred model we introduced here in the paper. Recently there is some work are done on machine learning techniques and proceed along.

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