Pasi Kuvaja

Bio: Pasi Kuvaja is an academic researcher from University of Oulu. The author has contributed to research in topics: Software development & Agile software development. The author has an hindex of 23, co-authored 83 publications receiving 1400 citations.

Dimensions of DevOps

Abstract: DevOps has been identified as an important aspect in the continuous deployment paradigm in practitioner communities and academic research circles. However, little has been presented to describe and formalize what it constitutes. The absence of such understanding means that the phenomenon will not be effectively communicated and its impact not understood in those two communities. This study investigates the elements that characterize the DevOps phenomenon using a literature survey and interviews with practitioners actively involved in the DevOps movement. Four main dimensions of DevOps are identified: collaboration, automation, measurement and monitoring. An initial conceptual framework is developed to communicate the phenomenon to practitioners and the scientific community as well as to facilitate input for future research.

Towards DevOps in the Embedded Systems Domain: Why is It So Hard?

Abstract: DevOps is a predominant phenomenon in the web domain. Its two core principles emphasize collaboration between software development and operations, and the use of agile principles to manage deployment environments and their configurations. DevOps techniques, such as collaboration and behaviour-driven monitoring, have been used by web companies to facilitate continuous deployment of new functionality to customers. The techniques may also offer opportunities for continuous product improvement when adopted in the embedded systems domain. However, certain characteristics of embedded software development present obstacles for DevOps adoption, and as yet, there is no empirical evidence of its adoption in the embedded systems domain. In this study, we present the challenges for DevOps adoption in embedded systems using a multiple-case study approach with four companies. The contribution of this paper is to introduce the concept of DevOps adoption in the embedded systems domain and then to identify key challenges for the DevOps adoption.

DevOps in practice: A multiple case study of five companies

Abstract: Context: DevOps is considered important in the ability to frequently and reliably update a system in operational state. DevOps presumes cross-functional collaboration and automation between software development and operations. DevOps adoption and implementation in companies is non-trivial due to required changes in technical, organisational and cultural aspects. Objectives: This exploratory study presents detailed descriptions of how DevOps is implemented in practice. The context of our empirical investigation is web application and service development in small and medium sized companies. Method: A multiple-case study was conducted in five different development contexts with successful DevOps implementations since its benefits, such as quick releases and minimum deployment errors, were achieved. Data was mainly collected through interviews with 26 practitioners and observations made at the companies. Data was analysed by first coding each case individually using a set of predefined themes and thereafter perform a cross-case synthesis. Results: Our analysis yielded some of the following results: (i) software development team attaining ownership and responsibility to deploy software changes in production is crucial in DevOps. (ii) toolchain usage and support in deployment pipeline activities accelerates the delivery of software changes, bug fixes and handling of production incidents. (ii) the delivery speed to production is affected by context factors, such as manual approvals by the product owner (iii) steep learning curve for new skills is experienced by both software developers and operations staff, who also have to cope with working under pressure. Conclusion: Our findings contributes to the overall understanding of DevOps concept, practices and its perceived impacts, particularly in small and medium sized companies. We discuss two practical implications of the results.

An Exploratory Study of DevOps: Extending the Dimensions of DevOps with Practices

Abstract: Software-intensive companies constantly try to improve their software development process for better software quality and a faster time to market. The DevOps phenomenon emerged with the promise of easing the process of putting new software changes to production at a fast rate whilst also increasing the learning and innovation cycles of their products. However, the DevOps phenomenon lacks clear definition and practices, and this makes it difficult for both researchers and practitioners to understand the phenomenon. In this paper, we focus on consolidating the understanding of DevOps and its practices as described by practitioners using multivocal literature and interviews. The study contributes to a scientific definition of DevOps and patterns of DevOps practices to help identify and adopt the phenomenon. Keywords–DevOps; Continuous Deployment; Agile.

Research Design: Qualitative, Quantitative, and Mixed Method Approaches

Abstract: หนงสอเรอง การออกแบบการวจย: วธการวจยเชงคณภาพ วธการวจยเชงปรมาณ และวธการวจยแบบผสม (Research Design: Qualitative, Quantitative, and Mixed Method Approaches) เปนหนงสอทเรยบเรยงเพออธบายเกยวกบความแตกตางของกระบวนทศนการวจยทง 2 แบบ ไดแก การวจย เชงปรมาณ และการวจยเชงคณภาพ และความจำเปนของประเดนปญหาการวจยทตองนำกระบวนทศนทง 2 มารวมกนหาขอคนพบเพอนำไปสผลการวจยทสามารถนำผลการวจยไปใชประโยชนไดอยางจรงมากยงขน เรยกวา “การวจยแบบผสมผสาน” ซงเปนหนงสอทอธบายวธการวจยทง 2 ประเภทไดอยางชดเจน และการรวมกนของกระบวนทศนการวจยทง 2 แบบอยางลงตว

Test Driven Development: By Example

Abstract: 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.

Toyota production system : beyond large-scale production

Abstract: * Starting from Need* Evolution of the Toyota Production System* Further Development* Genealogy of the Toyota Production System* The True Intention of the Ford System* Surviving the Low-Growth Period

The Lean Startup:How Today's Entrepreneurs Use Continuous Innovation to Create Radically Successful Businesses

Abstract: Eric Ries was born in September 1978. He graduated from Yale University and moved to silicon Valley in the beginning of the millennium. He pioneered the philosophy of Lean Startup, based on his experience with multiple startups, primary being IMVU which he co-founded along with Will Harvey in 2004. Eric Ries originated his Lean Startup philosophy after getting inspired from the Japanese Philosophy of Lean Manufacturing.