This is the second part of a two-part article introducing Jeff De Luca’s Feature Driven Development (FDD) process. In particular, we are looking at how FDD differs from Scrum and eXtreme Programming-inspired approaches when it comes to working with larger teams and projects. In the first part we briefly introduced the ‘just enough’ upfront activities that FDD uses to support the additional communication that inevitably is needed in a larger project/team. In the second part of the article we cover how FDD leverages the results of those upfront activities within the highly iterative, self-managing, organized-chaos that is the delivery engine room of an FDD project. The Engine Room: Delivering Frequent, Tangible Working Results Once there is an initial overall model (FDD Process #1), an initial overall features list (FDD Process #2), and an initial overall plan (FDD Process #3) in place, an FDD project is ready to start delivering the required software feature by feature. Peter Coad, the Chief Architect on the original FDD project used the phrase ‘Deliver frequent, tangible, working results’ as a mantra to impress upon people the idea of delivering real, completed, client-valued function as often as possible. Scrum and eXtreme Programming do this using fixed length iterations of a calendar month or 2-4 weeks. FDD is different. Each Chief Programmer (lead developer) runs a series of iterations, each of which is normally a matter of a few days, and never longer than two weeks. At the start of each of these iterations, each Chief Programmer selects the next few features that make sense to implement from the backlog of feature sets (activities) that were assigned to him or her in FDD Process #2. The Chief Programmer leads the development of these features through FDD processes #4 and #5, Design by Feature (DBF) and Build By Feature (BBF). Note that iterations through the DBF/BBF processes are not fixed length, and Chief Programmers do not synchronize the start and end of their iterations with each other. In addition, the DBF/BBF processes are always executed as a pair (FDD describes them as two separate processes rather than one combined process for psychological reasons). FDD Process #4: Design By Feature After selecting the features for the iteration, a Chief Programmer needs to form their feature team. Yes, feature teams are formed and disbanded for each iteration through the DBF/BBF process pair. Using the knowledge gained from the modeling process (FDD Process #1), the Chief Programmer identifies the domain classes that are likely to be involved in this iteration, and forms his or her feature team from the owners of those classes. In practice, this means: a feature team is small, typically 3 to 5 people, because features are small. By definition, a feature team comprises of all the class owners who need to modify their classes in the development of the features during that iteration. There is no need to wait for members of other teams to change code. Therefore, there are all the benefits of code ownership and a sense of collective ownership too. Class owners may find themselves a member of multiple feature teams at the same time. This does not happen as frequently as might be supposed because iterations are so short – days not weeks. When it does, it is not a big problem in practice. Chief Programmers work together to resolve any problematic conflicts and, with care, most developers can manage the demands of occasionally belonging to more than one feature team for a short time. Once formed, the Chief Programmer facilitates the collaborative analysis and design of the features for that iteration. Depending on the complexity, this may involve the team walking through the requirements in detail with a domain expert, and studying any existing relevant documents. It also involves agreeing on the interactions and other details that need to be added to the model to support the new features. The final step in the DBF part of the iteration is to review the design. For simple features, this may be a brief sanity check of the design held within the feature team. For more significant features, the Chief Programmer will typically involve other Chief Programmers or class owners so that they are aware and can comment on the impact of the proposed design. For small team projects, the object models are frequently small enough for individual or pairs of developers to create good designs while writing tests for a particular feature or user story. For larger projects, this is not necessarily the case and designs created purely by considering the tests a feature or user story must pass are more likely to be brittle and require significant refactoring. The DBF process in FDD ensures that the overall model also guides the design, helping to maintain its ‘conceptual integrity’ [Brooks]. FDD Process #5: Build By Feature The Build by Feature (BBF) part of the iteration involves the team members coding up the features, testing them at both unit level and feature level, and holding a code inspection before promoting the completed features into the project's regular build process. Testing FDD expects developers to unit test their code. It expects feature teams to test their features. FDD is not overly concerned with how this is achieved. Projects and feature teams are free to adopt the testing tools, frameworks, and level of formality and completeness that are most appropriate. FDD does not mind if tests are written before or after code. What FDD mandates, is that the feature team deliver code that has been appropriately tested and inspected. Only once the new features have passed testing and inspection is the source code allowed into the build process. Code Inspections Most people want to know why FDD mandates code inspections, especially those that have endured sitting through hours of boring, unproductive, ego-polishing/demolishing, point-scoring sessions that formed so-called code reviews, inspections or walkthroughs. The reason FDD mandates code inspections is that research has shown time and again that when done well, inspections find more defects and different kinds of defects than testing [McConnell]. Not only that but by examining the code of the more experienced, knowledgeable developers on the team and having them explain the idioms they use, less experienced developers learn better coding techniques. In addition, knowing that their code will be inspected and not be allowed in the build unless it conforms to the agreed standards encourages developers to pay more attention to conforming to those standards. One of the benefits of working in feature teams is that the whole feature team is on the hot seat during an inspection, not just one individual. This removes much of the intensity and anxiety inherent in inspecting one individuals work. The Chief Programmer decides on the level of formality of each inspection depending on the complexity and impact of the features developed in that iteration. Where the code has little or no impact outside the feature team, an inspection will usually only involve the feature team inspecting each other’s work. Where there is significant impact the Chief Programmer pulls in other Chief Programmers and developers to both verify the code and communicate the impact of the new features. eXtreme Programming acknowledges inspections as a ‘best practice' but promotes pair programming as the logical conclusion of applying this practice. Pair programming is obviously better than individual developers delivering code without any form of inspection. However, while FDD neither mandates nor forbids pair programming, a more-traditional inspection is: fresh eyes looking at the code, catching bad assumptions made by the coder/s a Chief Programmer present to ensure the techniques passed on are good. After all, developers can just as easily teach each other bad habits as well as good habits. a change of pace for developers, a chance to step away from the keyboard and mouse for a short while. With the wide availability of automated source code formatting and static analysis tools, code inspections can now be shorter, concentrating on the logic and coding idioms involved and not getting bogged down in nit-picking such as alignment of braces, etc. The Build FDD assumes some sort of regular build process. Some teams build weekly, others daily and others continuously. FDD avoids mandating any particular build regime. This enables the project team to apply the most applicable. If a continuous integration environment makes sense, then the team is free to employ the best there is. Progress Reports Agile projects like highly visible progress information. FDD projects are no exception. In fact, because larger projects frequently have higher profiles within an organization, presenting meaningful, accurate, timely project information appropriately at the different levels of leadership/management is even more important. Conventionally, FDD projects track the development of each feature through its DBF/BBF iteration against six milestones: domain walkthrough, design, design inspection, coding, testing and inspection, and promoted to build. For each feature, Chief Programmers record the actual date a milestone is reached. Tracking each feature through these six milestones enables the project to keep an eye on how much work is 'in progress'. Too many features at a particular milestone indicate a process problem. Those promoting Kanban and other Limited Work In Progress methods have formalized this idea to strictly define what is meant by 'too many' for each of their development iteration milestones/statuses. They then refuse to move an item to a new milestone/status if the limit on the number of items at that status has been reached. This forces a team to keep items moving forward through the process [Kanban]. FDD is not so formal, leaving the Chief Programmers and Development Manager to keep an eye informally on the amount of work in progress. The Big Wallchart, Burn-Down/Up Charts, Etc For general visibility of progress within a project, the team typically lists all the features in the project complete with their owning Chief Programmer, feature team members, and the dates of each milestone achieved on a suitable wall. In addition, features can be colored to show if they are started, in-progress, completed or blocked. This allows people to stand back from the wall and get a good visual feel for the overall status of the project. They can then walk up to the wall to zoom in on particular areas and activities in more detail. Recording the date each milestone is achieved enables a team to produce burn-down or burn-up charts analogous to those produced in Scrum and XP. Chief Programmers and Project Managers can determine from these if the underlying rate of feature completion is increasing, decreasing, or stable, etc. One of the best ways to achieve this is to have the Chief Programmers regularly (typically once a week) communicate progress to either the project manager or someone dedicated to the task. That person then produces whatever roll-up and burn-down charts desired. Having an administrative person, the equivalent of the Tracker role in eXtreme Programming, perform these report formatting duties frees the Chief Programmers to spend more time on making progress rather than formatting reports about it. Parking Lot Charts For reporting to senior management, the level of individual features is often too granular. Here, FDD projects typically use a graphical report format that known as the Parking Lot chart. In a Parking Lot chart, each group of ‘parking lots’ represents one of the subject areas from the features list. Each parking lot represents one of the activities within that subject area, and displays the name of that set of features, the number of features within it, and the percentage of those features that have been completed (typically both in text and using a progress bar). The parking lots are also colored to indicate whether the features in that activity have been started, completed, or have significant blockages. The FDD parking lot format has become so popular that Mike Cohn included it in his book, Agile Planning and Estimating [Cohn]. (click for larger image) Figure 1: Example Parking Lot Chart Conclusion Feature-Driven Development combines the key advantages of other popular agile approaches with model-centric techniques and other best practices that scale to much larger teams and projects. It defines three upfront activities that provide a conceptual and management framework within which a larger-than-usual agile team can add functionality to the software, feature by feature. It is also just as applicable for smaller teams tackling non-trivial problem domains where it is worth spending just a little time to sketch a map of the journey before dashing off down the agile coding highway. Even if you and your team decide not to adopt FDD as a whole, understanding why FDD is the way it is, can provide insight into scaling traditional agile approaches beyond small, largely independent teams. Finally, I would like to say thank you to Serguei Khramtchenko and Mark Lesk at Nebulon for their corrections and suggestions incorporated in this article. References [Brooks] Frederick P. Brooks, Jr., The Mythical Man-Month, Addison Wesley [Cohn] Cohn, Agile Planning and Estimating, Prentice-Hall PTR [FDD] FDD Community Site, www.featuredrivendevelopment.com/ [Kanban] The home of Kanban software development, www.limitedwipsociety.org/ [McConnell] McConnell, Code Complete, Microsoft [Nebulon] The Latest FDD Processes available from www.nebulon.com/articles/fdd/latestprocesses.html [Palmer-1] Palmer, Felsing, A Practical Guide to Feature-Driven Development, Prentice Hall PTR

One nice feature in JUnit 4 is that of Parameterized Tests, which let you do data-driven testing in JUnit with a minimum of fuss. It's easy enough, and very useful, to set up basic data-driven tests by defining your test data directly in your Java class. But what if you want to get your test data from somewhere else? In this article, we look at how to obtain test data from an Excel spreadsheet. Parameterized tests allow data-driven tests in JUnit. That is, rather than having different of test cases that explore various aspects of your class's (or your application's) behavior, you define sets of input parameters and expected results, and test how your application (or, more often, one particular component) behaves. Data-driven tests are great for applications involving calculations, for testing ranges, boundary conditions and corner cases. In JUnit, a typical parameterized test might look like this: @RunWith(Parameterized.class) public class PremiumTweetsServiceTest { private int numberOfTweets; private double expectedFee; @Parameters public static Collection data() { return Arrays.asList(new Object[][] { { 0, 0.00 }, { 50, 5.00 }, { 99, 9.90 }, { 100, 10.00 }, { 101, 10.08 }, { 200, 18}, { 499, 41.92 }, { 500, 42 }, { 501, 42.05 }, { 1000, 67 }, { 10000, 517 }, }); } public PremiumTweetsServiceTest(int numberOfTweets, double expectedFee) { super(); this.numberOfTweets = numberOfTweets; this.expectedFee = expectedFee; } @Test public void shouldCalculateCorrectFee() { PremiumTweetsService premiumTweetsService = new PremiumTweetsService(); double calculatedFees = premiumTweetsService.calculateFeesDue(numberOfTweets); assertThat(calculatedFees, is(expectedFee)); } } The test class has member variables that correspond to input values (numberOfTweets) and expected results (expectedFee). The @RunWith(Parameterzed.class) annotation gets JUnit to inject your test data into instances of your test class, via the constructor. The test data is provided by a method with the @Parameters annotation. This method needs to return a collection of arrays, but beyond that you can implement it however you want. In the above example, we just create an embedded array in the Java code. However, you can also get it from other sources. To illustrate this point, I wrote a simple class that reads in an Excel spreadsheet and provides the data in it in this form: @RunWith(Parameterized.class) public class DataDrivenTestsWithSpreadsheetTest { private double a; private double b; private double aTimesB; @Parameters public static Collection spreadsheetData() throws IOException { InputStream spreadsheet = new FileInputStream("src/test/resources/aTimesB.xls"); return new SpreadsheetData(spreadsheet).getData(); } public DataDrivenTestsWithSpreadsheetTest(double a, double b, double aTimesB) { super(); this.a = a; this.b = b; this.aTimesB = aTimesB; } @Test public void shouldCalculateATimesB() { double calculatedValue = a * b; assertThat(calculatedValue, is(aTimesB)); } } The Excel spreadsheet contains multiplication tables in three columns: The SpreadsheetData class uses the Apache POI project to load data from an Excel spreadsheet and transform it into a list of Object arrays compatible with the @Parameters annotation. I've placed the source code, complete with unit-test examples on BitBucket. For the curious, the SpreadsheetData class is shown here: public class SpreadsheetData { private transient Collection data = null; public SpreadsheetData(final InputStream excelInputStream) throws IOException { this.data = loadFromSpreadsheet(excelInputStream); } public Collection getData() { return data; } private Collection loadFromSpreadsheet(final InputStream excelFile) throws IOException { HSSFWorkbook workbook = new HSSFWorkbook(excelFile); data = new ArrayList(); Sheet sheet = workbook.getSheetAt(0); int numberOfColumns = countNonEmptyColumns(sheet); List rows = new ArrayList(); List rowData = new ArrayList(); for (Row row : sheet) { if (isEmpty(row)) { break; } else { rowData.clear(); for (int column = 0; column < numberOfColumns; column++) { Cell cell = row.getCell(column); rowData.add(objectFrom(workbook, cell)); } rows.add(rowData.toArray()); } } return rows; } private boolean isEmpty(final Row row) { Cell firstCell = row.getCell(0); boolean rowIsEmpty = (firstCell == null) || (firstCell.getCellType() == Cell.CELL_TYPE_BLANK); return rowIsEmpty; } /** * Count the number of columns, using the number of non-empty cells in the * first row. */ private int countNonEmptyColumns(final Sheet sheet) { Row firstRow = sheet.getRow(0); return firstEmptyCellPosition(firstRow); } private int firstEmptyCellPosition(final Row cells) { int columnCount = 0; for (Cell cell : cells) { if (cell.getCellType() == Cell.CELL_TYPE_BLANK) { break; } columnCount++; } return columnCount; } private Object objectFrom(final HSSFWorkbook workbook, final Cell cell) { Object cellValue = null; if (cell.getCellType() == Cell.CELL_TYPE_STRING) { cellValue = cell.getRichStringCellValue().getString(); } else if (cell.getCellType() == Cell.CELL_TYPE_NUMERIC) { cellValue = getNumericCellValue(cell); } else if (cell.getCellType() == Cell.CELL_TYPE_BOOLEAN) { cellValue = cell.getBooleanCellValue(); } else if (cell.getCellType() ==Cell.CELL_TYPE_FORMULA) { cellValue = evaluateCellFormula(workbook, cell); } return cellValue; } private Object getNumericCellValue(final Cell cell) { Object cellValue; if (DateUtil.isCellDateFormatted(cell)) { cellValue = new Date(cell.getDateCellValue().getTime()); } else { cellValue = cell.getNumericCellValue(); } return cellValue; } private Object evaluateCellFormula(final HSSFWorkbook workbook, final Cell cell) { FormulaEvaluator evaluator = workbook.getCreationHelper() .createFormulaEvaluator(); CellValue cellValue = evaluator.evaluate(cell); Object result = null; if (cellValue.getCellType() == Cell.CELL_TYPE_BOOLEAN) { result = cellValue.getBooleanValue(); } else if (cellValue.getCellType() == Cell.CELL_TYPE_NUMERIC) { result = cellValue.getNumberValue(); } else if (cellValue.getCellType() == Cell.CELL_TYPE_STRING) { result = cellValue.getStringValue(); } return result; } } Data-driven testing is a great way to test calculation-based applications more thoroughly. In a real-world application, this Excel spreadsheet could be provided by the client or the end-user with the business logic encoded within the spreadsheet. (The POI library handles numerical calculations just fine, though it seems to have a bit of trouble with calculations using dates). In this scenario, the Excel spreadsheet becomes part of your acceptance tests, and helps to define your requirements, allows effective test-driven development of the code itself, and also acts as part of your acceptance tests. From http://weblogs.java.net/blog/johnsmart

Feature-Driven Development (FDD) is one of the agile processes not talked or written about very much. Often mentioned in passing in agile software development books and forums, few actually know much about it. However, if you need to apply agile to larger projects and teams, it is worthwhile taking the time to understand FDD a little more The natural habitat of Scrum and XP-inspired approaches is a small team of skilled and disciplined developers. It remains a significant challenge to scale these approaches to larger projects and larger teams. Some have been successful but many have struggled. Feature-Driven Development (FDD) invented by Jeff De Luca is different. While just as applicable for small teams, Jeff designed FDD from the ground up to work for a larger team. Larger teams present different challenges. For example, a small team of disciplined and highly skilled developers by definition is likely to succeed regardless of which agile method they use. In contrast, it is unrealistic to expect that everyone in a larger team is equally skilled and disciplined. For this and other reasons, FDD makes different choices to Scrum and XP in a number of areas. In the first part of this two-part article, we briefly introduce the ‘just enough’ upfront activities that FDD uses to support the additional communication that inevitably is needed in a larger project/team. In the second part of the article, we cover how the highly iterative delivery part of FDD differs from Scrum and XP-inspired approaches. Iteration Zero:Getting Set to Deliver Most experienced agile teams are familiar with the concept of an iteration zero, a relatively short period for a team to put in place what they need to start delivering client-valued functionality in subsequent iterations. Despite general acceptance within the agile community that some form of iteration zero is a pragmatic necessity on most projects, neither Scrum nor eXtreme Programming formally have much to say about it. In contrast, an FDD project is organized around five 'processes', of which the first three can be considered roughly the equivalent of iteration zero activities. FDD does not use the term, iteration zero. It calls these three ‘processes’ initial project-wide activities. Each of the FDD processes is described so that it can be printed, in a typical-sized font, on no more than two sides of letter-sized paper. The most recent versions of the FDD processes are available from the FDD section of the Nebulon website, but very briefly an FDD project: … starts with the creation of a domain object model in collaboration with Domain Experts. Usinginformation from the modeling activity, and from any other requirements activities that have taken place, the developers go onto create a features list. Then a rough plan is drawn up and responsibilities assigned. Now we are ready to repeatedly take small groups of features through a design and build iteration that lasts no longer than two weeks and is often much shorter, sometimes only a matter of hours...[Palmer-1] FDD Process #1: Develop an Overall Model For many who have escaped from the perils of large, upfront analysis and design phases to the freedom and discipline of Scrum and eXtreme Programming-inspired approaches, the idea of developing a domain object model at the start of a project is controversial. In FDD, however, the building of an object model is not a long, drawn-out, activity performed by an elite few using expensive CASE tools. The modelers do not format the resulting model into a large document and throw it over the wall for developers to implement. Instead, building an initial object model in FDD is an intense, highly iterative, collaborative and generally enjoyable activity involving ‘domain and development members under the guidance of an experienced object modeler in the role of Chief Architect' [Nebulon]. FDD Process #1 describes the tasks and quality checks for executing this work, and while not mandatory, the object model is typically built using Peter Coad's modeling in color technique (modeling in color needs an introductory article all of its own [Palmer-2]). The idea is for both domain and development members of the team to gain a good, shared understanding of the problem domain. It is important that everyone understands the key problem domain concepts, relationships, and interactions. In doing so, the team as a whole learn to communicate with each other and start to establish a shared vocabulary, what Eric Evans calls a Ubiquitous Language [Evans]. The object model developed at this point concentrates on breadth rather than depth; depth is added iteratively through the lifetime of the project. The model is, therefore, a living artifact. Throughout the project, the model becomes the primary vehicle around which the team discusses, challenges, and clarifies requirements. FDD Process #2: Build a Features List With the first activity being to build an object model, some may conclude FDD is a model-driven process. It is not. While the model is central to the process, an FDD project is like a Scrum or eXtreme Programming project in being requirement-driven. Small, client-valued requirements referred to as features drive the project; the model merely helps guide. Formally, FDD defines a feature as a small, client-valued function expressed in the form: