การวจยเชงคณภาพ เปนเครองมอสำคญอยางหนงสำหรบทำความเขาใจสงคมและพฤตกรรมมนษย การวจยแบบการสรางทฤษฎจากขอมล กเปนหนงในหลายระเบยบวธการวจยเชงคณภาพทกำลงไดรบความสนใจ และเปนทนยมเพมสงขนเรอยๆ จากนกวชาการ และนกวจยในสาขาสงคมศาสตร และศาสตรอนๆ เชน พฤตกรรมศาสตร สงคมวทยา สาธารณสขศาสตร พยาบาลศาสตร จตวทยาสงคม ศกษาศาสตร รฐศาสตร และสารสนเทศศกษา ดงนน หนงสอเรอง “ConstructingGrounded Theory: A Practical Guide through Qualitative Analysis” หรอ “การสรางทฤษฎจากขอมล:แนวทางการปฏบตผานการวเคราะหเชงคณภาพ” จะชวยใหผอานมความรความเขาใจถงพฒนาการของปฏบตการวจยแบบสรางทฤษฎจากขอมล ตลอดจนแนวทาง และกระบวนการปฏบตการวจยอยางเปนระบบ จงเปนหนงสอทควรคาแกการอานโดยเฉพาะนกวจยรนใหม เพอเปนแนวทางในการนำความรความเขาใจไประยกตในงานวจยของตน อกทงนกวจยผเชยวชาญสามารถอานเพอขยายมโนทศนดานวจยใหกวางขวางขน

Constructing Grounded Theory: A Practical Guide through Qualitative Analysis

From the Book:
“Clean code that works” is Ron Jeffries’ pithy phrase. The goal is clean code that works, and for a whole bunch of reasons:
Clean code that works is a predictable way to develop. You know when you are finished, without having to worry about a long bug trail.Clean code that works gives you a chance to learn all the lessons that the code has to teach you. If you only ever slap together the first thing you think of, you never have time to think of a second, better, thing. Clean code that works improves the lives of users of our software.Clean code that works lets your teammates count on you, and you on them.Writing clean code that works feels good.But how do you get to clean code that works? Many forces drive you away from clean code, and even code that works. Without taking too much counsel of our fears, here’s what we do—drive development with automated tests, a style of development called “Test-Driven Development” (TDD for short). 
In Test-Driven Development, you:
Write new code only if you first have a failing automated test.Eliminate duplication.

Two simple rules, but they generate complex individual and group behavior. Some of the technical implications are:You must design organically, with running code providing feedback between decisionsYou must write your own tests, since you can’t wait twenty times a day for someone else to write a testYour development environment must provide rapid response to small changesYour designs must consist of many highly cohesive, loosely coupled components, just to make testing easy

The two rules imply an order to the tasks ofprogramming:
1. Red—write a little test that doesn’t work, perhaps doesn’t even compile at first
2. Green—make the test work quickly, committing whatever sins necessary in the process
3. Refactor—eliminate all the duplication created in just getting the test to work


Red/green/refactor. The TDD’s mantra.

Assuming for the moment that such a style is possible, it might be possible to dramatically reduce the defect density of code and make the subject of work crystal clear to all involved. If so, writing only code demanded by failing tests also has social implications:
If the defect density can be reduced enough, QA can shift from reactive to pro-active workIf the number of nasty surprises can be reduced enough, project managers can estimate accurately enough to involve real customers in daily developmentIf the topics of technical conversations can be made clear enough, programmers can work in minute-by-minute collaboration instead of daily or weekly collaborationAgain, if the defect density can be reduced enough, we can have shippable software with new functionality every day, leading to new business relationships with customers


So, the concept is simple, but what’s my motivation? Why would a programmer take on the additional work of writing automated tests? Why would a programmer work in tiny little steps when their mind is capable of great soaring swoops of design? Courage.
Courage
Test-driven development is a way of managing fear during programming. I don’t mean fear in a bad way, pow widdle prwogwammew needs a pacifiew, but fear in the legitimate, this-is-a-hard-problem-and-I-can’t-see-the-end-from-the-beginning sense. If pain is nature’s way of saying “Stop!”, fear is nature’s way of saying “Be careful.” Being careful is good, but fear has a host of other effects:
Makes you tentativeMakes you want to communicate lessMakes you shy from feedbackMakes you grumpy

None of these effects are helpful when programming, especially when programming something hard. So, how can you face a difficult situation and:
Instead of being tentative, begin learning concretely as quickly as possible.Instead of clamming up, communicate more clearly.Instead of avoiding feedback, search out helpful, concrete feedback.(You’ll have to work on grumpiness on your own.) 

Imagine programming as turning a crank to pull a bucket of water from a well. When the bucket is small, a free-spinning crank is fine. When the bucket is big and full of water, you’re going to get tired before the bucket is all the way up. You need a ratchet mechanism to enable you to rest between bouts of cranking. The heavier the bucket, the closer the teeth need to be on the ratchet.

The tests in test-driven development are the teeth of the ratchet. Once you get one test working, you know it is working, now and forever. You are one step closer to having everything working than you were when the test was broken. Now get the next one working, and the next, and the next. By analogy, the tougher the programming problem, the less ground should be covered by each test.

Readers of Extreme Programming Explained will notice a difference in tone between XP and TDD. TDD isn’t an absolute like Extreme Programming. XP says, “Here are things you must be able to do to be prepared to evolve further.” TDD is a little fuzzier. TDD is an awareness of the gap between decision and feedback during programming, and techniques to control that gap. “What if I do a paper design for a week, then test-drive the code? Is that TDD?” Sure, it’s TDD. You were aware of the gap between decision and feedback and you controlled the gap deliberately.

That said, most people who learn TDD find their programming practice changed for good. “Test Infected” is the phrase Erich Gamma coined to describe this shift. You might find yourself writing more tests earlier, and working in smaller steps than you ever dreamed would be sensible. On the other hand, some programmers learn TDD and go back to their earlier practices, reserving TDD for special occasions when ordinary programming isn’t making progress.

There are certainly programming tasks that can’t be driven solely by tests (or at least, not yet). Security software and concurrency, for example, are two topics where TDD is not sufficient to mechanically demonstrate that the goals of the software have been met. Security relies on essentially defect-free code, true, but also on human judgement about the methods used to secure the software. Subtle concurrency problems can’t be reliably duplicated by running the code.

Once you are finished reading this book, you should be ready to:
Start simplyWrite automated testsRefactor to add design decisions one at a time

This book is organized into three sections.
An example of writing typical model code using TDD. The example is one I got from Ward Cunningham years ago, and have used many times since, multi-currency arithmetic. In it you will learn to write tests before code and grow a design organically.An example of testing more complicated logic, including reflection and exceptions, by developing a framework for automated testing. This example also serves to introduce you to the xUnit architecture that is at the heart of many programmer-oriented testing tools. In the second example you will learn to work in even smaller steps than in the first example, including the kind of self-referential hooha beloved of computer scientists.Patterns for TDD. Included are patterns for the deciding what tests to write, how to write tests using xUnit, and a greatest hits selection of the design patterns and refactorings used in the examples.

I wrote the examples imagining a pair programming session. If you like looking at the map before wandering around, you may want to go straight to the patterns in Section 3 and use the examples as illustrations. If you prefer just wandering around and then looking at the map to see where you’ve been, try reading the examples through and refering to the patterns when you want more detail about a technique, then using the patterns as a reference.

Several reviewers have commented they got the most out of the examples when they started up a programming environment and entered the code and ran the tests as they read.

A note about the examples. Both examples, multi-currency calculation and a testing framework, appear simple. There are (and I have seen) complicated, ugly, messy ways of solving the same problems. I could have chosen one of those complicated, ugly, messy solutions to give the book an air of “reality.” However, my goal, and I hope your goal, is to write clean code that works. Before teeing off on the examples as being too simple, spend 15 seconds imagining a programming world in which all code was this clear and direct, where there were no complicated solutions, only apparently complicated problems begging for careful thought. TDD is a practice that can help you lead yourself to exactly that careful thought.

Test Driven Development: By Example

Readings in information retrieval.

Laser-induced incandescence: Particulate diagnostics for combustion, atmospheric, and industrial applications

Context: Scientists have become increasingly reliant on software in order to perform research that is too time-intensive, expensive, or dangerous to perform physically Because the results produced by the software drive important decisions, the software must be correct and developed efficiently Various software engineering practices have been shown to increase correctness and efficiency in the development of traditional software It is unclear whether these observations will hold in a scientific contextObjective: This paper evaluates claims from software engineers and scientific software developers about 12 different software engineering practices and their use in developing scientific softwareMethod: We performed a systematic literature review examining claims about how scientists develop software Of the 189 papers originally identified, 43 are included in the literature review These 43 papers contain 33 different claims about 12 software engineering practicesResults: The majority of the claims indicated that software engineering practices are useful for scientific software development Every claim was supported by evidence (ie personal experience, interview/survey, or case study) with slightly over half supported by multiple forms of evidence For those claims supported by only one type of evidence, interviews/surveys were the most common The claims that received the most support were: "The effectiveness of the testing practices currently used by scientific software developers is limited" and "Version control software is necessary for research groups with more than one developer" Additionally, many scientific software developers have unconsciously adopted an agile-like development methodologyConclusion: Use of software engineering practices could increase the correctness of scientific software and the efficiency of its development While there is still potential for increased use of these practices, scientific software developers have begun to embrace software engineering practices to improve their software Additionally, software engineering practices still need to be tailored to better fit the needs of scientific software development

/pdf/claims-about-the-use-of-software-engineering-practices-in-449tuyd09q.pdf

Claims about the use of software engineering practices in science



Open-source software (OSS) has become an important choice for developing software applications, and its usage has exponentially increased in recent years. Although many OSS systems have shown high reliability in terms of their functionality, they often exhibit several quality issues. Since most developers focus primarily on meeting clients’ functional requirements within the appropriate deadlines, the outcome suffers from poor design and implementation practices. This issue can also manifest as software code smells, resulting in a variety of quality issues such as software maintainability, comprehensibility, and extensibility. Generally speaking, OSS developers use code reviews during their software development to discover flaws or bugs in the updated code before it is merged with the code base. Nevertheless, despite the harmful impacts of code smells on software projects, the extent to which developers do consider them in the code review process is unclear in practice.



Open-source software (OSS) has become an important choice for developing software applications, and its usage is exponentially increasing in recent years. While many OSS systems have shown high reliability in terms of their functionality, they often exhibit several quality issues. Since most developers focus primarily on meeting clients’ functional requirements within the appropriate deadlines, the outcome suffers from poor design and implementation practices.



To better understand the code review process in OSS projects, we gathered the comments of code reviewers who specified where developers should fix code smells in two OSS projects, OpenStack and WikiMedia, between 2011 and 2015.



Since most developers focus primarily on meeting clients’ functional requirements within the appropriate deadlines, the outcome suffers from poor design and implementation practices. This problem can further manifest in the form of software code smells leading to multiple quality issues including software maintainability, comprehensibility, and extensibility.



Our findings indicate that most code reviewers do not pay much attention to code smells. Only a few code reviewers have attempted to motivate developers to improve their source code quality in general. The results also show that there is an increasing tendency to provide advice concerning code smells corrections over time.



We believe that this study's findings will encourage developers to use new software engineering practices, such as refactoring, to reduce code smells when developing OSS.



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An Exploratory Study on Code Smells During Code Review in OSS Projects: A Case Study on OpenStack and WikiMedia

Today, open-source software (OSS) is used in various applications. It has played a vital role in information systems of many user groups such as commercials, research, education, public health, and tourism . It is also a source of additional knowledge for collaborators because this type of software is easily accessible through websites that provide management of version control services such as GitHub. However, a recent study shows an increasing trend in the existence of code smells . In OSS, there is a growing number of code smells that cause software errors. Having a code smell in software is a serious issue since it impacts the software in terms of deployment, maintenance as well as user confidence toward the software. Finding code smells in the early stages of software development would provide for better software maintenance and reliability; thus, researchers invented the Zsmell software system that helps search for code smells in the source code saved in GitHub. Developed systems display data related to code smells in each source code version that was modified by collaborators. Thus, the developers will be able to employ the proper refactoring method, which is a change in the internal structure of software without changing the original functionality of the software . We believe that this system will enable open source collaborators to improve the quality of their OSS, especially on code smell reduction and the understanding of various types of code smell commonly found in OSS projects .

Zsmell – Code Smell Detection for Open Source Software

The tourism industry has continued to play an important role in the Thai economy. It is an industry that brings foreign currency to spend in the country's economy, causing employment, driving the country's economy. But with the advent of the digital era, travelers' behavior has changed by using digital technology as a tool to find information about attractions, places to stay, and traveler preferences. So, if those who operate tourism businesses and government agencies that govern tourism-related policies can understand what social media is talking about. It will be very useful in clarifying, understanding, developing, improving, as well as looking for opportunities for the tourism business in the future, but social media data is large unstructured data and difficult to manipulate by humans. Therefore, our idea was to study the classification of tourism opinion topics using the Latent Dirichlet Allocation (LDA) technique and to develop a digital platform for displaying the classification and trends of tourism opinion topics. Phuket is suitable as a case study because Phuket is one of the country's top tourism income earners, thus being regarded as an important area in Thailand's tourism industry. The aim of this study is to develop a digital platform, which will enable stakeholders to understand the underlying issues and interests of the online community and show trends in opinion issues from the past to the present that can be used to develop, improve, and seek new opportunities in tourism. It can be a resource for other researchers to use in further research.

Classification of Tourism-Related Topics in Phuket: A Case Study

Event-based aspect-oriented programming (EAOP) model allows to systematically treat relationship between point-cuts and to execute aspects whenever receiving events emitted by the base program. However, this model neither comes with formal specification nor provides any mechanism to verify its properties formally. In this paper, we propose a model-based method based on Event-B formal method to analyze an EAOP application. First, we specify its components in Event-B notations where we make use of Event-B refinement mechanism to model the base and the monitoring programs. After that, we exploit Event-B generated proof obligations to check if the application constraints are affected by aspect cross-cuts. Finally, the proposed method is illustrated in detail with an example.

A Model-Based Method for Modeling and Verifying Event-Based Aspect-Oriented Applications

Teaching programming courses in an academic environment has challenges, particularly for undergraduate students. These challenges can also be found in the software industry, where novice developers still need to obtain coding training. In practice, lecturers or trainers always assign exercises to students with the goal of improving their coding skills. Based on teaching experience, we have found that students may need to rely on software development kits to code an application faster, such as reusing lines of code already developed (code snippets) to solve a particular problem. Additionally, to reduce the software development time, novice developers tend to use crowdsourcing and social media sources of example code that can be used to solve programming problems. Utilizing these code snippets not only saves time for students or novice developers but also helps them to learn. Therefore, we proposed a system, called Squirrel, that is capable of offline and online code snippet storage and searching. Additionally, for online functionalities, this application supports searching for code snippets that exist in question-and-answer websites, such as Stack Overflow. We believe that this system will help increase the effectiveness of software development, especially for computer-science students and novice developers.

Aziz Nanthaamornphong

Papers

An Exploratory Study on Code Smells During Code Review in OSS Projects: A Case Study on OpenStack and WikiMedia

Zsmell – Code Smell Detection for Open Source Software

Classification of Tourism-Related Topics in Phuket: A Case Study

A Model-Based Method for Modeling and Verifying Event-Based Aspect-Oriented Applications

Squirrel: A Code Snippet Repository