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How to Choose a Development Methodology

One of our most viewed blog entries is PDLC or SDLC, while we don’t know definitively why we suspect that technology leaders are looking for ways to improve their organization’s performance e.g. better quality, faster development. etc. Additionally, we often get asked the question “what is the best software development methodology?” or “should we change PDLC’s?” The simple answer is that there is no “best” and changing methodologies is not likely to fix organizational or process problems. What I’d like to do in this posts is 1) give you a very brief overview of the different methodologies – consider this a primer, a refresher, or feel free to skip and 2) provide a framework for considering which methodology is right for your organization.

The waterfall model is an often used software development processes that occurs in a sequential set of steps. Progress is seen as flowing steadily downwards (like a waterfall) through the phases such as Idea, Analysis, Design, Development, Testing, Implementation and Maintenance. The waterfall development model originated in the hardware manufacturing arena where after-the-fact changes are prohibitively costly. Since no formal software development methodologies existed at the time, this hardware-oriented model was adapted for software development. There are many variations on the waterfall methodology that changes the phases but all have the similar quality of a sequential set of steps. Some waterfall variations include those from Six Sigma (DMAIC, DMADV).

Agile software development is a group of software development methodologies based on iterative and incremental development. The requirements and ultimate products or services that get delivered evolve through collaboration between self-organizing, cross-functional teams. This methodology promotes adaptive planning, evolutionary development and delivery. The Agile Manifesto was introduced in 2001 and introduced a conceptual framework that promotes foreseen interactions throughout the development cycle. The time boxed iterative approach encourages rapid and flexible response to change. There are many variations of Agile including XP, Scrum, FDD, DSDM, and RUP.

With so many different methodologies available how do you decide which is right for your team? Here are a few questions that will help guide you through the decision.

1) Is the business willing to be involved in the entire product development cycle? This involvement takes the form of dedicated resources (no split roles such as running a P&L by day and being a product manager by night), everyone goes through training, and joint ownership of the product / service (no blaming technology for slow delivery or quality problems).
YES – Consider any of the agile methodologies.
NO – Bypass all forms of agile. All of these require commitment and involvement by the business in order to be successful.

2) What is the size of your development teams? Is the entire development team less than 10 people or have you divided the code into small components / services that teams of less than 10 people own and support?
YES – Consider XP or Scrum flavors of the agile methodology.
NO – Consider FDD and DSDM which are capable of scaling up to 100 developers. If you team is even larger consider RUP. Note that with agile, when the development team gets larger so does the amount of documentation and communication and this tends to make the project less agile.

3) Are your teams located in the same location?
YES – Consider any flavor of agile.
NO – While remote teams can and do follow agile methodologies it is much more difficult. If the business owners are not collocated with the developers I would highly recommend sticking with a waterfall methodology.

4) Are you hiring a lot of developers?
YES – Consider the more popular forms of agile or waterfall to minimize the ramp up time of new developers coming on board. If you really want an agile methodology, consider XP which includes paired programming as a concept, and is a good way to bring new developers up to speed quickly.
NO – Any methodology is fine.

A last important idea is that it isn’t important to follow a pure flavor of any methodology. Purist or zealots of process or technology are dangerous because a single tool in your tool box doesn’t provide the flexibility needed in the real world. Feel free to mix or alter concepts of any methodology to make it fit better in your organization or the service being provided.

There are of course counter examples to every one of these questions, in fact I can probably give the examples from our client list. These questions/answers are not definitive but they should provide a starting point or framework for how you can determine your team’s development methodology.


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The Agile Executive

In this third installment of our “Agile Organization” series we discuss the qualities and attributes necessary for someone to lead a group of cross functional Agile teams in the development of a web-centric product.  For the purposes of this discussion, the Agile Executive is the person responsible for leading a group of agile teams in the development of a product.

In a world with less focus on functional organizations such as the one we’ve described in our Agile Organization articles, it is imperative that the leadership have a good understanding of all domains from the overall business through product management and finally each of the technical domains.  Clearly such an individual can’t be an expert in every one of these areas, but they should be deep in at least one and broad through all of them.  Ideally this would have been the case in the functional world as well, but alas functional organizations exist to support deep rather than broad domain knowledge.  In the Agile world we need deep in at least area and broad in all areas.

Such a deep yet broad individual could come from any of the functional domains.  The former head of product management may be one such candidate assuming that he or she had good engineering and operations understanding.  The head of engineering and operations may be heads of other agile teams, assuming that they have a high business acumen and good product understanding.  In fact, it should not matter whence the individual comes, but rather whether he or she has the business acumen, product savvy and technical chops to lead teams.

In our view of the world, such an individual will likely have a strong education consisting of an undergraduate STEM (science, technology, engineering or math) degree.  This helps give them the fundamentals necessary to effectively interact with engineers and add value to the engineering process.  They will also have likely attended graduate school in a business focused program such as an MBA with a curriculum that covers finance, accounting, product and strategy.  This background helps them understand the language of business.  The person will hopefully have served for at least a short time in one of the engineering disciplines as an individual contributor to help bridge the communication chasm that can sometimes exist between those who “do” and those who “dream”.  As they progress in their career, they will have taken on roles within product or worked closely with product in not only identification of near term product elements, but the strategic evaluation of product needs longer term as well.

From the perspective of philosophy, the ideal candidates are those who understand that innovation is more closely correlated with collaboration through wide networks than it is to the intelligence of one individual or a small group of people.  This understanding helps drive beneficial cognitive conflict and increased contribution to the process of innovation rather than the closed minded approach of affective conflict associated with small groups of contributors.

In summary, it’s not about whence the person comes but rather “who the person is”.  Leading cross disciplinary teams requires cross disciplinary knowledge.  As we can’t possibly experience enough personally to be effective in all areas, we must broaden ourselves through education and exposure and deepen ourselves through specific past experiences.  Most importantly, for a leader to succeed in such an environment he or she must understand that “it’s not about them” – that success is most highly correlated with teams that contribute and not with just being “wickedly smart”.


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The Agile Organization Solution

Having discussed why organizations arranged along functional boundaries (e.g. production or engineering, operations, infrastructure, QA, etc) and multi-disciplinary Agile teams are often at odds and counterproductive, we now propose a method of organizing product teams more aligned with the Agile process and Agile methods.

The potential solution is really very simple.  The design goals behind the solution are: maximization of individual capabilities of any given team to quickly and efficiently create solutions and solve problems; creation of ownership of individual products (or portions of products) and the business success metrics associated with that product in order to maximize innovation; maximization of morale through a sense of ownership and delegation of decision making; and reduction in time to market through the elimination of non-value added conflict.

The solution is clear given these goals – eliminate functional organizations and align the organization to architecture specific services.   In the ideal case, each of the senior leaders of these organizations are capable of owning and leading one more complete agile teams.   The number of teams that an executive has is likely going to depend on the size of the company, the size of the engineering and product teams and the number of discrete services in the company’s product architecture.

Here we come to a very important point – it is critically important that the architecture, the process and the organization structure be aligned to reap the benefits of such a change.  If the product architecture continues to be monolithic, nothing is solved.  The solution described above will get you no further than an “agile overlay across functional organizations”.  Each division of agile teams needs to own their own services so that disputes, problems and opportunities can be resolved or capitalized within the team.  This rapid resolution starts to slow down when outside resources are necessary to resolve a problem or capitalize on an opportunity.

We readily admit that this new approach to eliminating functional organizations in favor of agile teams isn’t for everyone.  Some companies don’t have the need to segment their architectures into services as they aren’t experiencing rapid growth.  As such, they shouldn’t pay the price of re-architecting their product.  Some companies deliver to very clearly defined specifications and as a result can’t really benefit from the product discovery aspects inherent to Agile or the questionable “final dates”.  These companies are probably better off continuing to develop in a waterfall fashion.  Many companies won’t have the right skill sets at the top to have former functional executives own product sections.

This last issue sets up a topic for another post.  The question we aim to answer is “What are the qualities and attributes of the Agile Executive?”


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Enterprise Cloud Computing – Book Review

A topic of particular interest to us is cloud computing so I picked up a copy of Gautum Shroff’s Enterprise Cloud Computing: Technology, Architecture, Applications published in 2010 by Cambridge University Press. Overall I enjoyed the book and thought it covered some great topics but there were a few topics that I wanted the author to cover in more depth.

Enterprise Cloud Computing Book Cover

The publisher states that the book is “intended primarily for practicing software architects who need to assess the impact of such a transformation.” I would recommend this book for architects, engineers, and managers who are not currently well versed with cloud computing. For individuals who already possess a familiarity on these subject this will not be in depth enough nor will it have enough practical advice on when to consider the different applications.

Of minor issue to me is that this book spends a good deal of time upfront covering the evolution of the internet into a cloud computing platform. A bigger issue to me is that coverage of topics is done very well at an academic or theoretical level but doesn’t follow through enough on the practical side. For example, Shroff’s coverage of topics such as MapReduce in Chapter 11 are thorough in describing how the internal functionality but fall short on when, how, or why to actually implement them in an enterprise architecture. In this 13 page chapter, he unfortunately only gives one page to the practical application of batch processing using MapReduce. He revisits this topic in other chapters such as Chapter 16 “Enterprise analytics and search” and does an excellent job explaining how it works but his coverage of the when, how, or why this should be implemented is not given enough attention.

He picks up the practical advice in the final Chapter 18 “Roadmap for enterprise cloud computing”. Here he suggests several ways companies should consider using cloud and Dev 2.0 (Force.com and TCS InstantApps). I would like to have seen this practical side implemented throughout the book.

I really enjoyed Shroff”s coverage of the economics of cloud computing in Chapter 6. He addresses the issue by showing how he compares the in-house (collocation center) vs cloud. Readers can adopt his approach using their own numbers to produce a similar comparison.

The book does a great job covering the fundamentals of enterprise computing, including a technical introduction to enterprise architecture. It will of interest to programmers and software architects who are not yet familiar with these topics. It is suggested by the publisher that this book could serve as a reference for a graduate-level course in software architecture or software engineering, I agree.


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Evolving Architecture And Software

Is your software and architecture aligned? Ensuring that they are aligned is one of the key elements in managing complex software systems.

When asked by a team what they should prepare for an engagement with us, we usually tell them to not prepare anything. Instead of PowerPoint slides showing the architecture, network, etc we prefer for people to jump to the white board and draw. One of the primary reasons is that we often find people debating how the architecture actually exists. How does your architecture diagrams or institutional knowledge reflect reality of the software?

In the May issue of Computer, there is an article, “Evolving Software Architecture Descriptions of Critical Systems”, by Tom Mens, Jeff Magee, and Bernhard Rumpe, in which the authors’ state:

An explicit architecture description is important but not sufficient to manage the complexity of developing, maintaining, and evolving a critical software-intensive system.

The authors continue explaining that the architecture description must be accurate and traceably linked to the actual implementation in software so that changes in the architecture are reflected in implementation and vice versa.

If your team has spent a bunch of time creating an architecture that will scale, all that effort is wasted when the software implementation doesn’t abide by the architecture. Because of the ever evolving nature of complex software systems it is admittedly difficult to keep the architecture description and software artifacts aligned.  The authors of the article suggest that evolving architecture descriptions requires co-evolution of different viewpoints such as the structural and behavioral. To this I completely agree but they address the solution to this issue from the aspect of Architecture Description Languages (ADLs). The problem with this approach is that I don’t know of many, if any, SaaS companies using ADLs. Therefore, in order to accomplish this co-eveolution of software artifacts and architecture descriptions we have to seek a different solution.

To ensure that architectural changes are reflected in the software we typically suggest that companies rely on architecture principles. We’ve dedicated an entire chapter in The Art of Scalability to this subject but I’ll try to summarize it here. Architectural principles are a set of ideas that the team has determined when used as guidelines during the design and development of the software will yield a scalable, available, and cost effective system. Principles should help influence the behavior and the culture of the team. We often use the SMART acronym to describe good principles as being Specific, Measurable, Achievable, Realistic, and Testable.

So how about the other direction, how do we ensure the architecture description accurately reflects the software? By using JAD and ARB processes, which we’ve covered in detail before on this blog as well as in the book, we can help ensure that software artifacts that deviate from the established architecture are discussed and noted by the appropriate individuals and teams.

Remember that the co-evolution of the software as well as the architecture design is critical in order to manage the development and maintenance of complex, critical software systems. Implement simple but efficient processes to ensure these remain synchronized.

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Agile Architects

If you think agile development methods and architecture are at odds, think again. They can not only coexist but can thrive together to build better products and platforms.

We recently posted what agile is not, where we outlined questions that we often hear about agile development. Another question that is often raised is how to combine the seemingly long-term process of architecture with the short-term nature of agile development. We believe that architecture is not at odds with agile development and that the two can not only coexist but complement each other. To ensure your architecture standards are being integrated with each sprint, resulting in a scalable and available architecture, we rely on the Joint Architecture Development (JAD).

We’ve covered JAD before but as a recap, this is the process by which features are designed in a series of meetings where developers, architects, and tech operations come together to create the design. This multi-function representation early in the development process ensures that individuals are aware of standards, there is buy-in from all concerned parties, and that the design is benefitted by the knowledge that exists in different technical fields.

In the IEEE Software article “Architects as Service Providers,” Roland Faber says that “the architect role is to champion system qualities and stability, while the developer role is to champion the system functions and change.” The agile architect interacts frequently and flexibly with the developers, building a trust relationship with them.

The JAD is ideal for flexible interaction that can happen in short bursts of effort that correspond to sprints. The agile architect must understand that, because of the nature of agile development, architecture must be dynamic and not static. Architects must rely on personal interaction with developers not documentation to understand the requirements.

Faber continues in his article describing two phases of the architecture process as preparation and support. During preparation the architect engages in processes such as prepare rules, frameworks, and structures. During support the architect helps resolve conflicts, engages in firefighting, and stimulates architecture communication. He makes a point that if the developers don’t believe the architects will provide support they won’t tell them when they are breaking the rules.


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Data Access Layers

Ways of abstracting the storage of data have been around for a long time.  In data warehouses engineers abstract data into business or domain objects that can be manipulated in reports.  For object oriented programming, engineers can use the active record pattern to create a wrapper class representing a table and methods for inserting, updating, or deleting. Thus the manipulation of the database rows are abstracted into the object oriented parlance of classes and methods. This layer of computing is known as a Data Access Layer (DAL) and hides the complexity of the underlying data store from engineers who do not need to be bothered with those details.

There are many frameworks or object-relational mapping (ORM) tools for creating DAL’s for different programming languages such as Active Record for Ruby and Hibernate for Java.  We often hear from development teams that have adopted an ORM that the pros include: shorter development time, better designed code and reduction in amount of code.

However, a quick search will show you that not everyone is sold on the benefits of a DAL. The reduction in code is debatable when constructing complex queries or considering that Hibernate is over 800 KLOC of Java and XML.  There are also concerns about the ability to scale effectively when using DALs.  While it is possible with an ORM to scale on the X-axis such as with MySQL master-slave replication, the Y and Z axes splits can become much more complicated.

I am a fan of DAL’s for their centralization of data objects and their abstraction of relational data into objects. To me these advantages speed development and testing time and improve quality. Additionally, given the sophistication and open source of ORM’s today, I think it makes sense to consider using one as a framework. However, if you choose to do so, you need to consider ahead of time how you would shard your data along other axes. The time for those considerations is moved up when using an ORM. Think of the D-I-D approach where the cost to make a change during the Design phase is negligible compared to changes made after Deployment.


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PDLC or SDLC

As a frequent technology writer I often find myself referring to the method or process that teams use to produce software. The two terms that are usually given for this are software development life cycle (SDLC) and product development life cycle (PDLC). The question that I have is are these really interchangeable? I don’t think so and here’s why.

Wikipedia, our collective intelligence, doesn’t have an entry for PDLC, but explains that the product life cycle has to do with the life of a product in the market and involves many professional disciplines. According to this definition the stages include market introduction, growth, mature, and saturation. This really isn’t the PDLC that I’m interested in. Under new product development (NDP) we find a defintion more akin to PDLC that includes the complete process of bringing a new product to market and includes the following steps: idea generation, idea screening, concept development, business analysis, beta testing, technical implementation, commercialization, and pricing.

Under SDLC, Wikipedia doesn’t let us down and explains it as a structure imposed on the development of software products. In the article are references to multiple different models including the classic waterfall as well as agile, RAD, and Scrum and others.

In my mind the PDLC is the overarching process of product development that includes the business units. The SDLC is the specific steps within the PDLC that are completed by the technical organization (product managers included). An image on HBSC’s site that doesn’t seem to have any accompanying explanation depicts this very well graphically.

Another way to explain the way I think of them is to me all professional software projects are products but not all product development includes software development.  See the Venn diagram below. The upfront (bus analysis, competitive analysis, etc) and back end work (infrastructure, support, depreciation, etc) are part of the PDLC and are essential to get the software project created in the SDLC out the door successfully.  There are non-software related products that still require a PDLC to develop.

Do you use them interchangeably?  What do you think the differences are?


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From Technician to Engineer

We’ve had a couple posts on this topic of engineer vs craftsman vs technician but I found myself in the past couple of days discussing this topic in two different settings and thought it would be fun to revisit on the blog. I started the conversation with a post that quoted Tom DeMarco concluding that “software engineering is an idea whose time has come and gone.” Also quoted was Jeff Atwood stating that “If you want to move your project forward, the only reliable way to do that is to cultivate a deep sense of software craftsmanship and professionalism around it.” Marty picked up the conversation in another post stating that:

All of this goes to say that software developers rarely “engineer” anything – at least not anymore and not in the sense defined by other engineering disciplines. Most software developers are closer to the technicians within other engineering disciplines; they have a knowledge that certain approaches work but do not necessarily understand the “physics” behind the approach. In fact, such “physics” (or the equivalent) rarely exist. Many no longer even understand how compilers and interpreters do their jobs (or care to do so).

I think it is possible that we are seeing the evolution of our discipline as it struggles to determine what its final form will take. Computer science, information technology, software engineering, and other related disciplines are all relatively new fields of study. With a new discipline it should be expected that definitions and themes will need to be stretched or reconsidered.

Software engineers, similar to other engineering disciplines who are taught more “true” laws such as Newton’s or Faraday’s, also undergo something of an apprenticeship once their degree is conferred. Mechanical and electrical engineers work beside senior engineers who help them transition from the theoretical to the practical. Software engineers are often apprenticed in the same manner by more senior engineers. If the practical implementation of one discipline is considered engineering because it is based upon laws and principles, I would argue that the principles of architecture for scalability are of a similar nature. This in fact I think is a strong differentiator between technicians and engineers within the software development discipline. A technician can write code, setup a database, or administer a server. An engineer can architect a database or system or pool of servers such that it can scale. We’ve written several posts about the principles and patterns of scalability and a large part of our book is dedicated to these principles. Are these sufficiently established to be called a principle as defined in Wikipedia?

A principle is one of several things: (a) a descriptive comprehensive and fundamental law, doctrine, or assumption; (b) a normative rule or code of conduct, and (c) a law or fact of nature underlying the working of an artificial device

I still like the idea of software developers as craftsmen and -women but as I concluded in the other post, that discussion for me is as much about organizational size and control as it is anything else. The technician vs engineer discussion I think is best held in the light of are they or are they not applying laws or principles. As the American Engineers’ Council for Professional Development defines engineering “The creative application of scientific principles to design or develop…” Have we as a discipline, especially in terms of scalability, advanced enough to call what we use “principles”? Let us know what you think.


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Engineering or Craftsmanship

Having gone through a computer science program at a school that required many engineering courses such as mechanical engineering, fluid dynamics, and electrical engineering as part of the core curriculum, I have a good appreciation of the differences between classic engineering work and computer science. One of the other AKF partners attending this same program along with me and we often debate whether our field should be considered an engineering discipline or not.

Jeff Atwood posted recently about how floored he was reading Tom DeMarco’s article in IEEE Software, where Tom stated that he has come to the conclusion that “software engineering is an idea that whose time has come and gone.” Tom DeMarco is one of the leading contributors on software engineering practices and has written such books as Controlling Software Projects: Management, Measurement, and Estimation, in which it’s first line is the famously quoted “You can’t control what you can’t measure.” Tom has come to the conclusion that:

For the past 40 years, for example, we’ve tortured ourselves over our inability to finish a software project on time and on budget. But as I hinted earlier, this never should have been the supreme goal. The more important goal is transformation, creating software that changes the world or that transforms a company or how it does business….Software development is and always will be somewhat experimental.

Jeff concludes his post with this statement, “…control is ultimately illusory on software development projects. If you want to move your project forward, the only reliable way to do that is to cultivate a deep sense of software craftsmanship and professionalism around it.”

All this reminded me of a post that Jeffrey Zeldman made about design management in which he states:

The trick to great projects, I have found, is (a.) landing clients with whom you are sympatico, and who understand language, time, and money the same way you do, and (b.) assembling teams you don’t have to manage, because everyone instinctively knows what to do.

There seems to be a theme among these thought leaders that you cannot manage your way into building great software but rather you must hone software much like a brew-master does for a micro-brew or a furniture-maker does for a piece of furniture. I suspect the real driver behind this notion of software craftsmanship is that it if you don’t want to have to actively manage projects and people you need to be highly selective in who joins the team and limit the size of the team. You must have management and process in larger organizations, no matter how professional and disciplined the team. There is likely some ratio of professionalism and size of team where if you fall below this your projects breakdown without additional process and active management. As in the graph below, if all your engineers are apprentices or journeymen and not master craftsmen they would be lower on the craftsmanship axis and you could have fewer of them on your team before you required some increased process or control.

Continuing with the microbrewery example, you cannot provide the volume and consistency of product for the entire beer drinking population of the US with four brewers in a 1,000 sq ft shop. You need thousands of people with management and process. The same goes for large software projects, you eventually cannot develop and support the application with a small team.

But wait you say, how about large open source projects? Let’s take Wikipedia, perhaps the largest open project that exists. Jimbo Wales, the co-founder of Wikipedia states “…it turns out over 50% of all the edits are done by just .7% of the users – 524 people. And in fact the most active 2%, which is 1400 people, have done 73.4% of all the edits.” Considering there are almost 3 million English articles in Wikipedia, this means each team working on a article is very small, possibly a single person.

Speaking of Wikipedia, one of those 524 people defines software engineer as “a person who applies the principles of software engineering to the design, development, testing, and evaluation of the software and systems that make computers or anything containing software, such as chips, work.” To me this is too formulaic and doesn’t describe accurately the application of style, aesthetics, and pride in one’s work. I for one like the notion that software development is as much craftsmanship as it is engineering and if that acknowledgement requires us to give up the coveted title of engineer so be it. But I can’t let this desire to be seen as a craftsman obscure the concept that the technology organization has to support the business as best as possible. If the business requires 750 engineers then there is no amount of proper selection or craftsmanship that is going to replace control, management, measurement, and process.

Perhaps not much of a prophecy but I predict the continuing divergence among software development organizations and professionals. Some will insist that the only way to make quality software is in small teams that do not require management nor measurement while others will fall squarely in the camp of control, insisting that any sizable project requires too many developers to not also require measurements. The reality is yes to both, microbrews are great but consumers still demand that a Michelob purchased in Wichita taste the same as it does in San Jose. Our technological world will be changed dramatically by a small team of developers but at the same time we will continue to run software on our desktops created by armies of engineers.

What side of the argument do you side with engineer or craftsman?


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