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Identifying and Managing Technical Debt
 

Identifying and Managing Technical Debt

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The technical debt metaphor is useful in capturing the long-term impacts of ...

The technical debt metaphor is useful in capturing the long-term impacts of
tradeoffs taken during software maintenance between productivity (getting
something done sooner) and maintainability (degradation of the code's
quality over time). This webinar on Technical Debt will present
techniques and insights that help software engineers to identify and track
technical debt in their projects. We will outline how business and product
quality goals should affect the choice of approaches (and combinations of
approaches) for managing technical debt. More specifically, we will discuss
a set of automated approaches based on static code analysis that are likely
to spot problems in source code that have real impact on productivity and
defect proneness. Based on previous empirical studies, we will give further
advice on which types of debt can be found by these tools, and which types
are not yet detectable.

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    Identifying and Managing Technical Debt Identifying and Managing Technical Debt Presentation Transcript

    • Identifying and ManagingTechnical DebtDr. Nico ZazworkaFraunhofer Center forExperimental Software EngineeringDr. Carolyn SeamanUniversity of MarylandBaltimore Countyhttp://www.technicaldebt.umbc.edu/
    • Outline• Introduction to the Technical Debt metaphor • Definition and examples of everyday debt • Benefits of explicitly managing Technical Debt• Technical Debt Framework • Technical Debt properties: principal vs. interest • Recording and communicating Technical Debt• Identifying important Technical Debt • Design debt: Code smells, Grime, ASA issues, Modularity violations • Other types of Technical Debt• Management strategies to pay down Technical Debt 2• Outlook
    • Software Maintenance• Large inventory of operational systems that need to be maintained • Fixed • Enhanced • Adapted• Such systems need constant modification in order to remain useful• Most such systems are too expensive to replace, so considerable resources go into their maintenance• However, maintenance, even more than development, is characterized by tight budget and time constraints 3
    • Technical Debt• Technical Debt is the gap between: 4
    • Technical Debt• Technical Debt is the gap between: • Making a change perfectly • Preserving architectural design • Employing good programming practices and standards • Updating the documentation • Testing thoroughly 5
    • Technical Debt• Technical Debt is the gap between: • And making the change work • As quickly as possible • With as few resources as possible 6
    • Everyday Indicators of Technical Debt 7
    • Everyday Indicators of Technical Debt“Don’t worry about the documentation for now.” 8
    • Everyday Indicators of Technical Debt“Don’t worry about the documentation for now.” “The only one who can change this code is Carl” 9
    • Everyday Indicators of Technical Debt“Don’t worry about the documentation for now.” “The only one who can change this code is Carl” “It’s ok for now but we’ll refactor it later!” 10
    • Everyday Indicators of Technical Debt“Don’t worry about the documentation for now.” “The only one who can change this code is Carl” “It’s ok for now but we’ll refactor it later!”“ToDo/FixMe: this should be fixed before release” 11
    • Everyday Indicators of Technical Debt“Don’t worry about the documentation for now.” “The only one who can change this code is Carl” “It’s ok for now but we’ll refactor it later!”“ToDo/FixMe: this should be fixed before release” “Let’s just copy and paste this part.” 12
    • Everyday Indicators of Technical Debt “Don’t worry about the documentation for now.” “The only one who can change this code is Carl” “It’s ok for now but we’ll refactor it later!”“ToDo/FixMe: this should be fixed before release” “Let’s just copy and paste this part.”“Does anybody know where we store the database access password?” 13
    • Everyday Indicators of Technical Debt “Don’t worry about the documentation for now.” “The only one who can change this code is Carl” “It’s ok for now but we’ll refactor it later!”“ToDo/FixMe: this should be fixed before release” “Let’s just copy and paste this part.”“Does anybody know where we store the database access password?” “I know if I touch that code everything else breaks!” 14
    • Everyday Indicators of Technical Debt “Don’t worry about the documentation for now.” “The only one who can change this code is Carl” “It’s ok for now but we’ll refactor it later!” “ToDo/FixMe: this should be fixed before release” “Let’s just copy and paste this part.” “Does anybody know where we store the database access password?” “I know if I touch that code everything else breaks!”“Let’s finish the testing in the next release.” 15
    • Everyday Indicators of Technical Debt “Don’t worry about the documentation for now.” “The only one who can change this code is Carl” “It’s ok for now but we’ll refactor it later!” “ToDo/FixMe: this should be fixed before release” “Let’s just copy and paste this part.” “Does anybody know where we store the database access password?” “I know if I touch that code everything else breaks!”“Let’s finish the testing in the next release.” 16 “The release is coming up, so just get it done!”
    • Technical Debt Metaphor A metaphor, NOT a theory or a scientific concept 17
    • Technical Debt Metaphor • Definition • Incomplete, immature, or inadequate artifact in the software development lifecycle (Cunningham, 1992) • Aspects of the software we know are wrong, but don’t have time to fix now • Tasks that were left undone, but that run a risk of causing future problems if not completed 18
    • Technical Debt Metaphor • Benefits • Higher software productivity in the current release • Lower cost of current release 19
    • Technical Debt Metaphor • Benefits • Higher software productivity in the current release • Lower cost of current release • Costs • “Interest” – increased maintenance costs • Risk that the debt gets out of control 20
    • Technical Debt Metaphor • Benefits • Higher software productivity in the current release • Lower cost of current release • Costs • “Interest” – increased maintenance costs • Risk that the debt gets out of control • Little scientific research, but • Discussions in blogs, forums, etc. • Strongly related to Risk Management 21
    • How Technical Debt is Managed (implicitly) Wow, this module is really bad. It’s going to be very hard to make any changes to it. David Miriam 22 Developer Manager
    • How Technical Debt is Managed (implicitly) Hey, Miriam, I think we should take some time to refactor this module in the next release. David Miriam 23 Developer Manager
    • How Technical Debt is Managed (implicitly) Why would we do that? That would take a lot of time and effort. David Miriam 24 Developer Manager
    • How Technical Debt is Managed (implicitly) But if we don’t refactor it soon, I have a gut feeling it’s going to cause major problems later. David Miriam 25 Developer Manager
    • How Technical Debt is Managed (implicitly) David is pretty smart, and he’s usually right about these kinds of things. David Miriam 26 Developer Manager
    • How Technical Debt is Managed (implicitly) David Miriam 27 Developer OK, I’ll put in the plan for Manager the next release.
    • How Technical Debt is Managed (implicitly) Another Example 28
    • How Technical Debt is Managed (implicitly)Wow, this module is really bad. It’sgoing to be very Hey, Miriam, I think wehard to make any should take some time to changes to it. refactor this module in the next release. David Miriam 29Developer Manager
    • How Technical Debt is Managed (implicitly) Those developers always try to make their code perfect. I need some evidence that this is worth it. David Miriam 30Developer Manager
    • How Technical Debt is Managed (implicitly) What is the ROI of this refactoring? David Miriam 31Developer Manager
    • How Technical Debt is Managed (implicitly) RO…WHAT?!? David Miriam 32Developer Manager
    • How Technical Debt is Managed (implicitly) David Miriam 33Developer Let’s stick with Manager implementing important features.
    • Potential Payoffs of ExplicitlyManaging TD• Lowered maintenance costs • Avoiding “interest payments” • Avoiding unnecessary “perfecting” work• Increased maintenance productivity • Better prioritization of tasks in each release • Maintenance always performed on code that is easier to work with• Avoiding surprises • Fewer components that fail without warning • Fewer unexpectedly large over-budget maintenance tasks • Better estimation of the costs and risks of postponing maintenance tasks 34
    • A Framework for ManagingTechnical Debt TD Estimation TD Decision Identification Making TD List 35
    • Technical Debt List A list of TD Items  Tasks that were left undone, but that run a risk of causing future problems if not completed.  Examples: Components/modules/classes that need refactoring, testing that needs to be done, etc. Content of TD Item  Description – what, where, why?  Principal – how much will it cost to do the work?  Interest – what happens if we don’t do this work?  Amount – amount of extra work if this causes problems later  Probability – probability that this will cause future problems TD List Update Policy 36 The TD list should be reviewed after each release, when items should be added as well as removed.
    • TD Item ExampleID 37Date 3/31/2008 (Release 3.2)Responsible Joe DeveloperType DesignLocation Method calculateStateTax in Module TaxCalcDescription In the last release, Joe added method calculateStateTax quickly and method is overly complex and not documented.Estimated principal Medium (medium level of effort to refactor and clean code)Estimated interest amount: High (it will be costly to make changes to the method in future, especially by other developers) 37Estimated interest probability High (it is very likely that this methods needs to be changed with each future release)
    • Some definitions• Principal • The cost of eliminating a Technical Debt instance RIGHT NOW• Interest • The cost, over some period of time, of NOT eliminating a Technical Debt instance • Interest is where the risk lies • Interest probability • The probability, over a given period of time, that a TD instance will increase the cost of some future activity • Interest amount • Assuming that a TD instance does in fact increase the cost of some activity, the amount of the increase • NOTE: Unlike financial debt, Technical Debt interest will change over time 38
    • Be careful when usingapproaches that quantify principal only 39
    • Be careful when using approaches that quantify principal only A statement such as:“Your project has $1,000,000 Technical Debt.” 40is only one side of the coin.
    • So….The goal of managing TD is… Eliminating all Technical Debt 41
    • So….The goal of managing TD is… Eliminating all Technical Debt NOT 42
    • So….The goal of managing TD is…To determine when• The amount of interestavoided justifies• The cost to pay off the principal 43
    • Example Scenario• Technical Debt: One of your code modules is in need of refactoring • TD Principal: Refactoring the entire module will cost $10,000• From past data you have established that: • This module is modified in 75% of all releases • The cost of changing this module has gone up 10% each time it’s been changed over its last 5 changes: • 5 changes ago cost $10,000 • Last change cost almost $15,000 44
    • Example Scenario (cont.)• Technical Debt Computation• For the next release • Principal for paying off debt: refactoring the module costs $10,000 • Interest: • Cost of the next change to the module • If refactored first: $10,000 • If not refactored first: $16,000 • Extra cost if not refactored: about $6000 • Interest avoided = interest amount * interest probability • $6,000 * .75 = $4500Principal Interest Decision:$10,000 > $4500 Ignore 45
    • Identifying Important Technical Debt 46
    • Technical Debt Framework TD TD Estimation Estimation TD TD Decision Decision Identification Identification Making Making TD List 47
    • What are your goals formanaging Technical Debt?• What are your pain points? • Avoiding defects • Better predictability • Higher maintenance productivity • Avoiding surprises • Making developers happy • Security, Portability, Efficiency ?• Motivations are driven by: • Business domain and market • Past mistakes 48
    • Types of Technical Debt• Design/code debt – can be identified by examining source code and/or related documentation • Happier developers and higher productivity • Fewer defects• Testing debt – planned tests that were not run, or known deficiencies in the test suite (e.g. low code coverage) • Fewer surprises and better predictability• Documentation debt – missing or inadequate documentation of any type • Higher productivity• Defect debt – known defects that are not fixed • Happier developers and higher productivity 49
    • The Technical Debt Landscape (under construction) Pain Points Types of Debt Tools Defect Code Reliability Debt Smells Maintain- Design ASA Tools ability Debt Documentation Modularity Portability Debt Violations Testing Manual Security Debt Inspection … … … 50Understanding your pain points will help to focus on the right types of Technical Debt.
    • Identifying Design Debt • ASA issues (line level) • Code smells (method and class level) • Grime (class interaction level) • Modularity violations (architecture level) 51
    • ASA Issues • ASA: Automatic Static (Code) Analysis • Identify problems on line level: 1 Person person = aMap.get("bob"); 2 if (person != null) { 3 person.updateAccessTime(); Potential Null 4 } Pointer Exception 5 String name = person.getName(); • Inexpensive • Point to the problem, suggest solution • Gaining significant traction in practice: • Used by Google to identify problems • Google Fixit Event 52Links: http://findbugs.sourceforge.net/
    • ASA Issue Types Many (thousands) issues identified: Many are false positives: • False warnings • Violation, but interest is negligible 53
    • ASA Issue Types Configure tools towards your pain points.Start with the Technical Debt that is linked to the high priority goals. Many (thousands) issues identified: Many are false positives: • False warnings • Violation, but interest is negligible 54
    • ASA Tools: Our Recommendation Project • Turn OFF all issue types in the ASA tool. Quality Business • Activate types “interest-driven”: Goals • Decide what your quality and business goals are. • Prioritize them based on: • Past experience (user stories) • Measurable impact • Research Results: • Multithread correctness and Correctness issues are located in classes with higher defect-proneness 55
    • Code Smells• Methods and classes that violate the principles of good object oriented design, e.g.: • Clearly defined single responsibility • Encapsulation • Information hiding • Few and clear interfaces • Proper use of inheritance• Code Smells point to potential problems: • require investigation and final judgment by developer 56 • Set of 20 more or less formally defined Code Smells available
    • Code Smells Explained • Automatic approaches have been proposed and implemented to automatically detect Code Smells in object oriented code • Based on Radu Marinescu’s Detection Strategies • For Java: CodeVizard and Marple • For C#: ReSharper, CodeRush, Gendarme, FxCop • Two code smells important for TD: • god class • dispersed coupling 57Further reading: http://www.codevizard.com
    • Code Smells: OurRecommendation• If avoiding defects and increasing maintenance productivity are issues of concern, then… • Start by detecting and examining God Classes and Dispersed Coupling code smells • Prioritize modules with these smells for refactoring efforts• Research focus: God Classes (concept is easy to understand) • God Classes are 5-7 times more change prone • God Classes are 4-17 times more defect prone• Baseline from our experience: most systems have 2%-8% God Classes• Dispersed Coupling code smell points to defect and 58 maintenance prone classes
    • Design Patterns and Grime• Design patterns promise code to be more maintainable and less defect prone • Describe how multiple classes work together• Design patterns can decay over time as systems evolve• Grime: accumulation of non-pattern code in classes following a design pattern • Rot: changes that break the integrity of a design pattern• Early results show that grime has a noticeable effect on testability • As grime builds up, more test cases break • In turn affects productivity during the testing phase 59 • Leads to testing debt
    • Modularity Violations• Organization of software systems: inter-dependent modules • Proper architecture leading to a clear structure of relationships promotes reuse of modules and smaller ripple effects.• Dependencies indicate how modules should change together: • Example: If the Model is changed, Controller A Model and Controller B might require changes. Controller Controller• Modularity Violations: recurring A B changes on classes within modules that are not depending on each other: View 1 View 3 • Example: Classes in View 1 and View 3 changing together 60 View 2
    • Modularity Violations Research • Studies have shown that modularity violations are an excellent indicator of defect prone classes and change prone classes. • Tool: CLIO (Drexel University) • When applied, with other TD detection approaches, to an open source system, the results for predicting defects were: • Also, modularity violations were highly correlated with 61 modules that developers later chose to refactorFurther reading: http://www.slideshare.net/miryung/icse-2011-research-paper-on-modularity-violations
    • Technical Debt Framework TD TD Estimation Estimation TD TD Decision Decision Identification Identification Making Making TD List 62
    • Testing Debt “Theres no tests for buttons other• Tests that were planned but: than <input type="submit"> yet. Im • not implemented pretty sure also <input type="button"> works if other • not executed <input>s work, but <button disabled="disabled">Text</button> • or they got lost should be tested separately.” http://code.google.com/p/robotframework-seleniumlibrary/issues/detail?id=163• Inadequate tests • Test cases not updated for “While updating the package of html5lib to 0.90 in Debian I new/changed functionality realized that the unit tests are gone. To ensure the keep the • Low coverage package in a good working shape while it transitions trough new• Can be detected by: Python versions and new versions of the modules it depends on, it would • Comparing test results with test be *very* appreciated if the unit plans tests would be shipped in the zipfile again.” • Code coverage tools http://code.google.com/p/html5lib/issues/detail?id=134&colspec=ID%2 0Type%20Status%20Priority%20Milestone%20Owner%20Summary%20P • Comparing requirements ort 63 changes with test suite changes
    • Documentation Debt• Documentation that is not “Except there is no such class or field in the SDK. It is outdated kept up-to-date, e.g. documentation that definitely needs to be updated.” • Installations and run http://code.google.com/p/android/issues/detail?id=8483 instructions • Architecture “There is not much documentation documentation available regarding the format of .xclangspec files. As a starting • Requirements and use case point, see for instance the outdated documentation at: documentation http://maxao.free.fr/xcode-plugin- • API documentation interface/specifications.html” http://code.google.com/p/go/source/browse/misc/xcode/go.xclangspec ?r=30b0c392132645259e053a2ba8904383a55bab03• Can be detected by: • Comparing code changes “This was apparently the old behavior and its changed with documentation now, but the documentation changes doesnt so say.” • Comment density metrics http://code.google.com/p/redis/issues/detail?id=514 64
    • Defect Debt “There are a couple of latent• Known defects that are not bugs in the linux TLS implementation. Im filing a yet fixed single issue because they are so small and easy to fix.” • Low priority defects http://code.google.com/p/dynamorio/issues/detail?id=358 • Low severity defects • Manifest rarely • Workarounds present• Can be detected by: • Examining defect repositories • Test results 65
    • Bottom line• Different techniques detect different instances and types of technical debt • No one approach is sufficient by itself • No one approach is the right one for everyone• The solution is a strategy based on • Business and development goals • Most painful types of debt • A combination of approaches that focus on the most pain• Don’t try to automate it all • Some kinds of technical debt can only be detected by humans • Most kinds of technical debt can only be interpreted by humans • No substitute for talking about it 66
    • Technical Debt in Decision Making 67
    • Technical Debt Framework TD Estimation TD Decision Identification Making TD List 68
    • TD Attributes Three attributes of a TD item  Principal  Interest probability  Interest amount Start with a rough estimate of the attribute values  High, Medium, Low Defer more precise estimation until data is available  Fault detection ability and defect density => testing debt  Cost of fixing a defect pre-release & post-release => defect debt  Time and effort for updating documentation => documentation debt 69
    • TD Attribute Estimation• Principal => historical effort data• Interest Probability => historical usage, change, and defect data • Example questions • How likely is it that a defect will occur in the untested part? • How likely is it that code containing a known error will be exercised? • A time element• Interest amount • Assume that the item has an effect on future work • Example questions • How much more it will cost to deal with defects later in the system’s lifetime than in testing?• These are all hard to estimate with any certainty• Historical data will help• Any estimation is better than the current method – “gut feeling” 70
    • Decision Making Scenario• Question • When and which technical debt items should be paid?• Example • Significant work is planned for component X in the next release, should we pay down some debt on component X at the same time?• Assumptions • There is an up-to-date TD list that is sorted by component and has high, medium, and low values for principal and interest estimates for each item.• ProcessSelect Re-evaluate Estimate Compare Add up 71
    • Other Decision Models• The proposed TD management strategy is based on a simple cost/benefit analysis• But TD occurs in complicated development and business scenarios • TD items are inter-related • Business factors are important, too • Prediction is hard• Other strategies for making decisions might be appropriate • Portfolio model • Options • AHP 72
    • So where are we? 73
    • Technical Debt Framework TD Estimation TD Decision Identification Making TD List 74
    • “Avoid being a perfectionist in a world of finite resources.” Forrest ShullInstead… Manage Technical Debt to make the imperfections • documented • explicit • not so scary 75
    • Summary• Technical Debt is a metaphor that describes a very real phenomenon• Provides a way to talk and reason about the difficulties of software maintenance• Technical Debt comes in a variety of forms, all of which can be detected in different ways• Technical Debt can be documented and tracked in a TD list• Management of Technical Debt must involve consideration of interest, not just principal• The types of Technical Debt that are relevant for a particular situation depends on past experience, organizational culture, and business environment.• While the research is still early, it does provide some guidance 76 as to Technical Debt identification strategy.
    • What’s next…• Identify your pain points• Decide what types of Technical Debt are relevant for you• Choose a small set of tools and indicators• Start a TD list – can use our template - probably some developers already have one• Track the history of the TD items revealed by the tools to see if they are detecting “real” debt• Refine release planning process to incorporate TD• Track your success in reducing the “pain”• Add new detection strategies to fill the gaps• Call us if you need help 77• Tell us how it’s going!
    • Contact us… Nico Zazworka zazworka@gmail.com Carolyn Seaman cseaman@umbc.edu 78
    • Acknowledgments• UMBC • Yuepu Guo, Ph.D. candidate • Rodrigo Spínola, Postdoctoral Researcher• Fraunhofer Center • Forrest Shull, Division Director • Many past, current, and future interns• Other academic collaborators • Yuanfang Cai, Drexel University • Clemente Izurieta, Montana State University • Antonio Vetró, Ph.D. student, Politecnico Di Torino, Italy • Sunny Wong, now at Siemens Healthcare 79
    • Acknowledgments (cont.)• Industrial collaborators• NSF Grant #0916699: Measuring and Monitoring Technical Debt 80
    • ReferencesAyewah, N. et al. (2007). Evaluating static analysis defect warnings on production software. In Proceedings of the 7th ACM SIGPLAN-SIGSOFT workshop on Program analysis for software tools and engineering - PASTE ’07. New York, New York, USA: ACM Press, pp. 1-8. Available at: http://dl.acm.org/citation.cfm?id=1251535.1251536 .Baldwin C. and Clark K. (2000). Design Rules, Vol. 1: The Power of Modularity. MIT Press.Brown N., Cai Y., Guo Y., Kazman R., Kim M., Kruchten P., Lim E., MacCormack A., Nord R., Ozkaya I., Sangwan R., Seaman C., Sullivan K., and Zazworka N. (2010). Managing technical debt in software-reliant systems. FoSER 2010: 47-52.Cunningham W. (1992). The WyCash Portfolio Management System. Addendum to the proceedings on Object-oriented programming systems, languages, and applications, pp. 29-30, 1992.Fowler M. (2003). Technical Debt. Available: http://www.martinfowler.com/bliki/TechnicalDebt.htmlGuéhéneuc Y.G., and Albin-Amiot H. (2001). Using Design Patterns and Constraints to Automate the Detection and Correction of Inter-Class Design Defects. Proc 39th International Conference and Exhibition Technology of Object Oriented Languages and Systems, pp. 296-305, 2001.Guo Y., Seaman C., Zazworka N., and Shull F. (2010). Domain-specific tailoring of code smells: an empirical study. International Conference on Software Engineering, ERA Track Cape Town, South Africa, May.Guo Y., and Seaman C. (2011). A Portfolio Approach to Technical Debt Management. Workshop on Managing Technical Debt. Hawaii, USA, May. 81
    • ReferencesGuo Y., Seaman, C., Gomes R., Cavalcanti A., Tonin G, Da Silva F.Q.B., Santos A.L.M., Siebra C. (2011). Tracking Technical Debt – an exploratory case study. International Conference on Software Maintenance, Williamsburg, VA, September.Izurieta C., and Bieman J.M. (2008). Testing Consequences of Grime Buildup in Object Oriented Design Patterns. 1st ACM-IEEE International Conference on Software Testing, Verification and Validation, ICST ’08, Lillehamer, Norway, April 2008.Kim S. and Ernst M. (2007). Prioritizing Warning Categories by Analyzing Software History. In Fourth International Workshop on Mining Software Repositories (MSR’07:ICSE Workshops 2007). IEEE, pp. 27-27. Available at: http://dl.acm.org/citation.cfm?id=1268983.1269041.Marinescu R. (2004). Detection strategies: Metrics-based rules for detecting design flaws. IEEE International Conference on Software Maintenance. Pp. 350–359.Markowitz H. (1952). Portfolio Selection. the Journal of Finance. Vol. 7, No. 1, pp. 77-91.Rothman J. (2006). An Incremental Technique to Pay Off Testing Technical Debt. Available: http://www.stickyminds.com/sitewide.asp?Function=edetail&ObjectType=COL&ObjectId=11011&tth=DYN &tt=siteemail&iDyn=2Saaty T. L. (1982). Decision making for leaders: The analytical hierarchy process for decision in a complex world. Belmot, California: Lifetime Learning Publications.Schanz T., and Izurieta C. (2010). Object Oriented Design Pattern Decay: A Taxonomy. 4th ACM-IEEE International Symposium on Empirical Software Engineering and Measurement, ESEM ’10, Bolzano-Bozen, 82 Italy, September 2010.
    • ReferencesSeaman C., and Guo Y. (2011). Measuring and Monitoring Technical Debt. ADVANCES IN COMPUTERS. Vol. 82, pp. 25–46.Sortino F. A., and Price L. N. (1994). Performance Measurement in a Downside Risk Framework. the Journal of Investing. Vol. 3, No. 3, pp. 59-64.Sullivan K., Chalasani P., Jha S and Sazawal V. (1999). Software Design as an Investment Activity: A Real Options Perspective in Real Options and Business Strategy: Applications to Decision Making. Risk Books, November.Vetró A., Torchiano M., and Morisio, M. (2010). Assessing the precision of FindBugs by mining Java projects developed at a university. In Mining Software Repositories (MSR), 7th IEEE Working Conference on. pp. 110-113.Vetró A., Morisio M, Torchiano, M. (2011). An Empirical Validation of FindBugs Issues Related to Defects. Evaluation and Assessment in Software Engineering 2011 (EASE 2011). Durham City, UK.Wong S., Cai Y., Kim M., and Dalton M. (2011). Detecting software modularity violations. Proc. 33th International Conference on Software Engineering. May 2011, pp. 411–420.Zazworka N., Shaw M., Shull F., Seaman C. (2011). Investigating the Impact of Design Debt on Software Quality. Workshop on Managing Technical Debt, Hawaii, USA, May. 83
    • Tools• Java (open source): • FindBugs: http://findbugs.sourceforge.net/ • PMD: http://pmd.sourceforge.net/pmd-5.0.0/ • Checkstyle: http://checkstyle.sourceforge.net/• C#/.NET (commercial) • ReSharper: http://www.jetbrains.com/resharper/ • FxCop: http://msdn.microsoft.com/en-us/library/bb429476.aspx • CodeRush: http://go.devexpress.com/CodeRushX.aspx• Multi-language Framework (open source) • Sonar: http://www.sonarsource.org/• List of static analysis tools (over 100 tools for more than 10 languages): • Wikipedia: http://en.wikipedia.org/wiki/List_of_tools_for_static_code_analysis 84
    • Further Reading and Survey• Further Reading: • Our Research Group’s website: http://www.technicaldebt.umbc.edu/ • OnTechnicalDebt http://www.ontechnicaldebt.com/• 5 minute online survey about common beliefs on TDhttp://tinyurl.com/TechnicalDebt 85