Basics of qualitative research: Grounded theory procedures and techniques

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

The major change in the second edition of this book is the addition of a new chapter on probabilistic retrieval. This chapter has been included because I think this is one of the most interesting and active areas of research in information retrieval. There are still many problems to be solved so I hope that this particular chapter will be of some help to those who want to advance the state of knowledge in this area. All the other chapters have been updated by including some of the more recent work on the topics covered. In preparing this new edition I have benefited from discussions with Bruce Croft, The material of this book is aimed at advanced undergraduate information (or computer) science students, postgraduate library science students, and research workers in the field of IR. Some of the chapters, particularly Chapter 6 * , make simple use of a little advanced mathematics. However, the necessary mathematical tools can be easily mastered from numerous mathematical texts that now exist and, in any case, references have been given where the mathematics occur. I had to face the problem of balancing clarity of exposition with density of references. I was tempted to give large numbers of references but was afraid they would have destroyed the continuity of the text. I have tried to steer a middle course and not compete with the Annual Review of Information Science and Technology. Normally one is encouraged to cite only works that have been published in some readily accessible form, such as a book or periodical. Unfortunately, much of the interesting work in IR is contained in technical reports and Ph.D. theses. For example, most the work done on the SMART system at Cornell is available only in reports. Luckily many of these are now available through the National Technical Information Service (U.S.) and University Microfilms (U.K.). I have not avoided using these sources although if the same material is accessible more readily in some other form I have given it preference. I should like to acknowledge my considerable debt to many people and institutions that have helped me. Let me say first that they are responsible for many of the ideas in this book but that only I wish to be held responsible. My greatest debt is to Karen Sparck Jones who taught me to research information retrieval as an experimental science. Nick Jardine and Robin …

Information retrieval

https://research.aalto.fi/files/11744469/1_s2.0_S0164121216300826_main.pdf

Challenges and success factors for large-scale agile transformations

We introduce Sapienz, an approach to Android testing that uses multi-objective search-based testing to automatically explore and optimise test sequences, minimising length, while simultaneously maximising coverage and fault revelation. Sapienz combines random fuzzing, systematic and search-based exploration, exploiting seeding and multi-level instrumentation. Sapienz significantly outperforms (with large effect size) both the state-of-the-art technique Dynodroid and the widely-used tool, Android Monkey, in 7/10 experiments for coverage, 7/10 for fault detection and 10/10 for fault-revealing sequence length. When applied to the top 1,000 Google Play apps, Sapienz found 558 unique, previously unknown crashes. So far we have managed to make contact with the developers of 27 crashing apps. Of these, 14 have confirmed that the crashes are caused by real faults. Of those 14, six already have developer-confirmed fixes.

/pdf/sapienz-multi-objective-automated-testing-for-android-2xckmssyie.pdf

Sapienz: multi-objective automated testing for Android applications

ContextSoftware industry has widely adopted Agile software development methods. Agile literature proposes a few key metrics but little is known of the actual metrics use in Agile teams. ObjectiveThe objective of this paper is to increase knowledge of the reasons for and effects of using metrics in industrial Agile development. We focus on the metrics that Agile teams use, rather than the ones used from outside by software engineering researchers. In addition, we analyse the influence of the used metrics. MethodThis paper presents a systematic literature review (SLR) on using metrics in industrial Agile software development. We identified 774 papers, which we reduced to 30 primary studies through our paper selection process. ResultsThe results indicate that the reasons for and the effects of using metrics are focused on the following areas: sprint planning, progress tracking, software quality measurement, fixing software process problems, and motivating people. Additionally, we show that although Agile teams use many metrics suggested in the Agile literature, they also use many custom metrics. Finally, the most influential metrics in the primary studies are Velocity and Effort estimate. ConclusionThe use of metrics in Agile software development is similar to Traditional software development. Projects and sprints need to be planned and tracked. Quality needs to be measured. Problems in the process need to be identified and fixed. Future work should focus on metrics that had high importance but low prevalence in our study, as they can offer the largest impact to the software industry.

Using metrics in Agile and Lean Software Development - A systematic literature review of industrial studies

As part of a Finnish research program, researchers interviewed 15 information and communications technology companies to determine the extent to which the companies adopted continuous deployment. They also aimed to find out why continuous deployment is considered beneficial and what the obstacles are to its full adoption. The benefits mentioned the most often were the ability to get faster feedback, the ability to deploy more often to keep customers satisfied, and improved quality and productivity. Despite understanding the benefits, none of the companies adopted a fully automatic deployment pipeline. The companies also had higher continuous-deployment capability than what they practiced. In many cases, they consciously chose to not aim for full continuous deployment. Obstacles to full adoption included domain-imposed restrictions, resistance to change, customer desires, and developers' skill and confidence.

The Highways and Country Roads to Continuous Deployment

Context: Software project failures are common. Even though the reasons for failures have been widely studied, the analysis of their causal relationships is lacking. This creates an illusion that the causes of project failures are unrelated. Objective: The aim of this study is to conduct in-depth analysis of software project failures in four software product companies in order to understand the causes of failures and their relationships. For each failure, we want to understand which causes, so called bridge causes, interconnect different process areas, and which causes were perceived as the most promising targets for process improvement. Method: The causes of failures were detected by conducting root cause analysis. For each cause, we classified its type, process area, and interconnectedness to other causes. We quantitatively analyzed which type, process area, and interconnectedness categories (bridge, local) were common among the causes selected as the most feasible targets for process improvement activities. Finally, we qualitatively analyzed the bridge causes in order to find common denominators for the causal relationships interconnecting the process areas. Results: For each failure, our method identified causal relationships diagrams including 130-185 causes each. All four cases were unique, albeit some similarities occurred. On average, 50% of the causes were bridge causes. Lack of cooperation, weak task backlog, and lack of software testing resources were common bridge causes. Bridge causes, and causes related to tasks, people, and methods were common among the causes perceived as the most feasible targets for process improvement. The causes related to the project environment were frequent, but seldom perceived as feasible targets for process improvement. Conclusion: Prevention of a software project failure requires a case-specific analysis and controlling causes outside the process area where the failure surfaces. This calls for collaboration between the individuals and managers responsible for different process areas.

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Perceived causes of software project failures - An analysis of their relationships

Context: Continuous delivery is a software development discipline in which software is always kept releasable. The literature contains instructions on how to adopt continuous delivery, but the adoption has been challenging in practice. Objective: In this study, a systematic literature review is conducted to survey the faced problems when adopting continuous delivery. In addition, we identify causes for and solutions to the problems. Method: By searching five major bibliographic databases, we identified 293 articles related to continuous delivery. We selected 30 of them for further analysis based on them containing empirical evidence of adoption of continuous delivery, and focus on practice instead of only tooling. We analyzed the selected articles qualitatively and extracted problems, causes and solutions. The problems and solutions were thematically synthesized into seven themes: build design, system design, integration, testing, release, human and organizational and resource. Results: We identified a total of 40 problems, 28 causal relationships and 29 solutions related to adoption of continuous delivery. Testing and integration problems were reported most often, while the most critical reported problems were related to testing and system design. Causally, system design and testing were most connected to other themes. Solutions in the system design, resource and human and organizational themes had the most significant impact on the other themes. The system design and build design themes had the least reported solutions. Conclusions: When adopting continuous delivery, problems related to system design are common, critical and little studied. The found problems, causes and solutions can be used to solve problems when adopting continuous delivery in practice.

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Problems, Causes and Solutions When Adopting Continuous Delivery - A Systematic Literature Review

We present a field study on how testers use knowledge while performing exploratory software testing (ET) in industrial settings. We video recorded 12 testing sessions in four industrial organizations, having our subjects think aloud while performing their usual functional testing work. Using applied grounded theory, we analyzed how the subjects performed tests and what type of knowledge they utilized. We discuss how testers recognize failures based on their personal knowledge without detailed test case descriptions. The knowledge is classified under the categories of domain knowledge, system knowledge, and general software engineering knowledge. We found that testers applied their knowledge either as a test oracle to determine whether a result was correct or not, or for test design, to guide them in selecting objects for test and designing tests. Interestingly, a large number of failures, windfall failures, were found outside the actual focus areas of testing as a result of exploratory investigation. We conclude that the way exploratory testers apply their knowledge for test design and failure recognition differs clearly from the test-case-based paradigm and is one of the explanatory factors of the effectiveness of the exploratory testing approach.

Juha Itkonen

Papers

Using metrics in Agile and Lean Software Development - A systematic literature review of industrial studies

The Highways and Country Roads to Continuous Deployment

Perceived causes of software project failures - An analysis of their relationships

Problems, Causes and Solutions When Adopting Continuous Delivery - A Systematic Literature Review

The Role of the Tester's Knowledge in Exploratory Software Testing