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• code metrics for .net code

  - by user20358

  While the code metrics tool gives a pretty good analysis of the code being analyzed, I was wondering if there was any such benchmark on acceptable standards for the following as well: Maximum number of types per assembly Maximum number of such types that can be accessible Maximum number of parameters per method Acceptable RFC count Acceptable Afferent coupling count Acceptable Efferent coupling count Any other metrics to judge the quality of .Net code by? Thanks for your time.

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• Integrating Code Metrics in TFS 2010 Build

  - by Jakob Ehn

  The build process template and custom activity described in this post is available here: http://cid-ee034c9f620cd58d.office.live.com/self.aspx/BlogSamples/CodeMetricsSample.zip Running code metrics has been available since VS 2008, but only from inside the IDE. Yesterday Microsoft finally releases a Visual Studio Code Metrics Power Tool 10.0, a command line tool that lets you run code metrics on your applications.  This means that it is now possible to perform code metrics analysis on the build server as part of your nightly/QA builds (for example). In this post I will show how you can run the metrics command line tool, and also a custom activity that reads the output and appends the results to the build log, and also fails he build if the metric values exceeds certain (configurable) treshold values. The code metrics tool analyzes all the methods in the assemblies, measuring cyclomatic complexity, class coupling, depth of inheritance and lines of code. Then it calculates a Maintainability Index from these values that is a measure f how maintanable this method is, between 0 (worst) and 100 (best). For information on hwo this value is calculated, see http://blogs.msdn.com/b/codeanalysis/archive/2007/11/20/maintainability-index-range-and-meaning.aspx. After this it aggregates the information and present it at the class, namespace and module level as well. Running Metrics.exe in a build definition Running the actual tool is easy, just use a InvokeProcess activity last in the Compile the Project sequence, reference the metrics.exe file and pass the correct arguments and you will end up with a result XML file in the drop directory. Here is how it is done in the attached build process template: In the above sequence I first assign the path to the code metrics result file ([BinariesDirectory]\result.xml) to a variable called MetricsResultFile, which is then sent to the InvokeProcess activity in the Arguments property. Here are the arguments for the InvokeProcess activity: Note that we tell metrics.exe to analyze all assemblies located in the Binaries folder. You might want to do some more intelligent filtering here, you probably don’t want to analyze all 3rd party assemblies for example. Note also the path to the metrics.exe, this is the default location when you install the Code Metrics power tool. You must of course install the power tool on all build servers. Using the standard output logging (in the Handle Standard Output/Handle Error Output sections), we get the following output when running the build: Integrating Code Metrics into the build Having the results available next to the build result is nice, but we want to have results integrated in the build result itself, and also to affect the outcome of the build. The point of having QA builds that measure, for example, code metrics is to make it very clear how the code being built measures up to the standards of the project/company. Just having a XML file available in the drop location will not cause the developers to improve their code, but a (partially) failing build will! To do this, we need to write a custom activity that parses the metrics result file, logs it to the build log and fails the build if the values frfom the metrics is below/above some predefined treshold values. The custom activity performs the following steps Parses the XML. I’m using Linq 2 XSD for this, since the XML schema for the result file is available, it is vey easy to generate code that lets you query the structure using standard Linq operators. Runs through the metric result hierarchy and logs the metrics for each level and also verifies maintainability index and the cyclomatic complexity with the treshold values. The treshold values are defined in the build process template are are sent in as arguments to the custom activity If the treshold values are exceeded, the activity either fails or partially fails the current build. For more information about the structure of the code metrics result file, read Cameron Skinner's post about it. It is very simpe and easy to understand. I won’t go through the code of the custom activity here, since there is nothing special about it and it is available for download so you can look at it and play with it yourself. The treshold values for Maintainability Index and Cyclomatic Complexity is defined in the build process template, and can be modified per build definition: I have taken the default value for these settings from my colleague Terje Sandström post on Code Metrics - suggestions for approriate limits. You’ll notice that this is quite an improvement compared to using code metrics inside the IDE, where Red/Yellow/Green limits are fixed (and the default values are somewaht strange, see Terjes post for a discussion on this) This is the first version of the code metrics integration with TFS 2010 Build, I will proabably enhance the functionality and the logging (the “tree view” structure in the log becomes quite hard to read) soon. I will also consider adding it to the Community TFS Build Extensions site when it becomes a bit more mature. Another obvious improvement is to extend the data warehouse of TFS and push the metric results back to the warehouse and make it visible in the reports.

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• Code Metrics: Number of IL Instructions

  - by DigiMortal

  In my previous posting about code metrics I introduced how to measure LoC (Lines of Code) in .NET applications. Now let’s take a step further and let’s take a look how to measure compiled code. This way we can somehow have a picture about what compiler produces. In this posting I will introduce you code metric called number of IL instructions. NB! Number of IL instructions is not something you can use to measure productivity of your team. If you want to get better idea about the context of this metric and LoC then please read my first posting about LoC. What are IL instructions? When code written in some .NET Framework language is compiled then compiler produces assemblies that contain byte code. These assemblies are executed later by Common Language Runtime (CLR) that is code execution engine of .NET Framework. The byte code is called Intermediate Language (IL) – this is more common language than C# and VB.NET by example. You can use ILDasm tool to convert assemblies to IL assembler so you can read them. As IL instructions are building blocks of all .NET Framework binary code these instructions are smaller and highly general – we don’t want very rich low level language because it executes slower than more general language. For every method or property call in some .NET Framework language corresponds set of IL instructions. There is no 1:1 relationship between line in high level language and line in IL assembler. There are more IL instructions than lines in C# code by example. How much instructions there are? I have no common answer because it really depends on your code. Here you can see some metrics from my current community project that is developed on SharePoint Server 2007. As average I have about 7 IL instructions per line of code. This is not metric you should use, it is just illustrative example so you can see the differences between numbers of lines and IL instructions. Why should I measure the number of IL instructions? Just take a look at chart above. Compiler does something that you cannot see – it compiles your code to IL. This is not intuitive process because you usually cannot say what is exactly the end result. You know it at greater plain but you don’t know it exactly. Therefore we can expect some surprises and that’s why we should measure the number of IL instructions. By example, you may find better solution for some method in your source code. It looks nice, it works nice and everything seems to be okay. But on server under load your fix may be way slower than previous code. Although you minimized the number of lines of code it ended up with increasing the number of IL instructions. How to measure the number of IL instructions? My choice is NDepend because Visual Studio is not able to measure this metric. Steps to make are easy. Open your NDepend project or create new and add all your application assemblies to project (you can also add Visual Studio solution to project). Run project analysis and wait until it is done. You can see over-all stats form global summary window. This is the same window I used to read the LoC and the number of IL instructions metrics for my chart. Meanwhile I made some changes to my code (enabled advanced caching for events and event registrations module) and then I ran code analysis again to get results for this section of this posting. NDepend is also able to tell you exactly what parts of code have problematically much IL instructions. The code quality section of CQL Query Explorer shows you how much problems there are with members in analyzed code. If you click on the line Methods too big (NbILInstructions) you can see all the problematic members of classes in CQL Explorer shown in image on right. In my case if have 10 methods that are too big and two of them have horrible number of IL instructions – just take a look at first two methods in this TOP10. Also note the query box. NDepend has easy and SQL-like query language to query code analysis results. You can modify these queries if you like and also you can define your own ones if default set is not enough for you. What is good result? As you can see from query window then the number of IL instructions per member should have maximally 200 IL instructions. Of course, like always, the less instructions you have, the better performing code you have. I don’t mean here little differences but big ones. By example, take a look at my first method in warnings list. The number of IL instructions it has is huge. And believe me – this method looks awful. Conclusion The number of IL instructions is useful metric when optimizing your code. For analyzing code at general level to find out too long methods you can use the number of LoC metric because it is more intuitive for you and you can therefore handle the situation more easily. Also you can use NDepend as code metrics tool because it has a lot of metrics to offer.

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• Mix metrics for June 14, 2010

  - by tim.bonnemann

  We've been busy working on a few improvements to Mix which we plan to roll out over the coming weeks. In the meantime, here are our latest community metrics once again: Registered Mix users (weekly growth) 64,769 (+0.9%) Active users (percent of total) Last 30 days: 4,682 (7.2%) Last 60 days: 8,251 (12.7%) Last 90 days: 11,936 (18.4%) Traffic (30-day) Visits: 13,674 Page views: 77,808 Twitter Followers: 3,451 List mentions: 205 User-generated content (30-day) New ideas: 29 New questions: 38 New comments: 167 Groups There are currently 1,440 Mix groups (requires login).

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• Mix metrics for May 11, 2010

  - by tim.bonnemann

  It's been a while, sorry about not keeping up. Here once again are our latest community metrics. Any questions or suggestions, please leave a comment. Thanks! Registered Mix users (weekly growth) 62,937 (+0.5%) Active users (percent of total) Last 30 days: 3,928 (6.2%) Last 60 days: 7,850 (12.5%) Last 90 days: 11,875 (18.9%) Traffic (30-day) Visits: 11,623 Page views: 57,846 Twitter Followers: 3,311 List mentions: 193 User-generated content (30-day) New ideas: 31 New questions: 72 New comments: 373 Groups There are currently 1,421 Mix groups (requires login).

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• Is measuring software project metrics popular in todays industry?

  - by Russ K

  I encountered a developer who wanted some outside advice on their teams project. I found out they're developing a huge software suite for the companies executives, project manager and developers that can calculate metrics automatically and graph them per iteration. As a student from a computer science background I know very little on metrics and their importance, but my questions are: Do most companies have some way, doesn't have to be an elegant program, to measure meaningful metrics? Which metrics, single or combined, help you narrow down your projects scope and estimates? As a person who analyzes metrics, how often do you base decisions off of them? IE. Tests failed per week is increasing drastically? Do you feel that the introduction of studying metrics has helped you understand the project better? Not sure why but the developers project intrigued me and I must know more. If y

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• Mix metrics for March 22, 2010

  - by tim.bonnemann

  Mix hit another major milestone this past week, surpassing 60,000 registered members. Registered Mix users (weekly growth) 60,662 (+0.8%) Active users (percent of total) Last 30 days: 4,571 (7.5%) Last 60 days: 8,945 (14.7%) Last 90 days: 11,479 (18.9%) Traffic (30-day) Visits: 12,371 Page views: 70,896 Twitter Followers: 3,117 List mentions: 146 User-generated content (30-day) New ideas: 32 New questions: 74 New comments: 378 Groups There are currently 1,394 Mix groups (requires login).

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• Mix metrics for April 5, 2010

  - by tim.bonnemann

  Our latest numbers... Registered Mix users (weekly growth) 61,374 (+0.6%) Active users (percent of total) Last 30 days: 4,317 (7.0%) Last 60 days: 8,638 (14.1%) Last 90 days: 12,481 (20.3%) Traffic (30-day) Visits: 11,893 Page views: 65,880 Twitter Followers: 3,169 List mentions: 146 User-generated content (30-day) New ideas: 36 New questions: 57 New comments: 394 Groups There are currently 1,402 Mix groups (requires login).

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• Data architecture for event log metrics?

  - by elliot42

  My service has a large ongoing number of user events, and we would like to do things like "count occurrence of event type T since date D." We are trying to make two basic decisions: What to store? Storing every event vs. only storing aggregates (Event log style) log every event and count them later, vs. (Time-series style) store a single aggregated "count of event E for date D" for every day Where to store the data In a relational database (particularly MySQL) In a non-relational (NoSQL) database In flat log files (collected centrally over the network via syslog-ng) What is standard practice / where can I read more about comparing the different types of systems? Additional details: The total event stream is large, potentially hundreds of thousands of entries per day But our current need is only to count certain types of events within it We don't necessarily need real-time access to the raw data or aggregation results IMHO, "log all events to files, crawl them at a later time to filter and aggregate the stream" is a pretty standard UNIX Way, but my Rails-y compatriots seem to think that nothing is real unless it's in MySQL.

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• Mix metrics for June 20, 2011

  - by tbonnema

  One of the busiest week's ever for Mix this past week, thanks to Suggest-a-Session contest, which just wrapped up at midnight Pacific last night. See for yourself: Registered Mix users (weekly growth) 76,378 (+1.7%) Active users (percent of total) Last 30 days: 4,383 (5.7%) Last 60 days: 5,232 (6.9%) Last 90 days: 6,240 (8.2%) Traffic (30-day) Visits: 17,368 Page views: 148,426 Twitter Followers: 7,116 List mentions: 380 User-generated content (30-day) New ideas: 10 New questions: 11 New comments: 30 New group messages: 34 New direct messages: 1,661 Groups There are currently 1,603 Mix groups (requires login).

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• Can defect containment metrics be readily applied at an organizational level when there is only a consistant organizational process framework?

  - by Thomas Owens

  Defect containment metrics, such as total defect containment effectiveness (TDCE) and phase containment effectiveness (PCE), can be used to give a good indicator of the quality of the process. TDCE captures the defects that are captured at some point between requirements and the release of a product into the field, indicating the overall effectiveness of the entire process to find and remove defects. PCE provides more detail at each phase of the software development life cycle and how the defect detection and removal techniques are working. Applying these metrics makes sense at a level where you have a well-defined process and methodology for product development, often a project. However, some organizations provide a process framework that is tailored at the project level. This process framework would include the necessary guidance for meeting certifications (ISO9001, CMMI), practices for incorporating known good techniques (agile methods, Lean, Six Sigma), and requirements for legal or regulatory reasons. However, the specific details of how to gather requirements, design the system, produce the software, conduct test, and release are left to the product development teams. Is there any effective way to apply defect containment metrics at an organizational level when only a process framework exists at the organizational level? If not, what might be some ideas for metrics that can be distilled from each project (each using a tailored process that fits into the organizational process framework) that captures defect containment metrics to discuss the ability of the process to find and remove defects? The end goal of such a metric would be to consolidate the defect containment practices of a large number of ongoing projects and report to management. The target audience would be people in roles such as the chief software engineer and the chief engineer (of all engineering disciplines) for the organization. Although project specific data would be available, the idea is to produce something that quantifies the general effectiveness of all tailored processes across all ongoing projects. I would suspect that this data would also be presented as part of CMMI, ISO, or similar audits to demonstrate process quality.

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• Software Metrics in Agile Methodologies

  - by geowa4

  Agile methodologies are rather prevalent these days, but I cannot seem to find much documentation on what metrics are most useful and why. I have found many more things saying that some traditional metrics like LOC and code coverage of tests are not appropriate, leaving two main questions: Why are those two (and other) metrics inappropriate? What metrics are best for Agile and why? Even with an Agile process, wouldn't you want to know how much code coverage you have with your unit tests? Or is it simply that this metric (and others) just are not as useful as other metrics like cyclomatic complexity and velocity?

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• server performance metrics report and practicality

  - by Anjesh

  I have a need of preparing web server (apache-php) performance report containing important metrics like CPU usage, disk io, memory usage on user basis. Couple of domains are hosted in the same server and they run from separate users using fcgi. The reason being sometimes some hosted applications take lots of cpu usage, making the server slow for other applications (running as separate users). i am planning to develop scripts for this, as i can't seem to find any simple utilities for this purpose. This script will take snapshots of the user wise metrics at defined periods say 15 minutes and record it. Any abnormalities will be reported via emails. How practical is that? also would be interesting to know what else need to be recorded.

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• Are CK Metrics still considered useful? Is there an open source tool to help?

  - by DeveloperDon

  Chidamber & Kemerer proposed several metrics for object oriented code. Among them, depth of inheritance tree, weighted number of methods, number of member functions, number of children, and coupling between objects. Using a base of code, they tried to correlated these metrics to the defect density and maintenance effort using covariant analysis. Are these metrics actionable in projects? Perhaps they can guide refactoring. For example weighted number of methods might show which God classes needed to be broken into more cohesive classes that address a single concern. Is there approach superseded by a better method, and is there a tool that can identify problem code, particularly in moderately large project being handed off to a new developer or team?

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• Is there any open source code analyzer for java which I can adopt my software metrics algorithm on it?

  - by daneshkohan

  I am doing my masters dissertation and I have conducted a software metrics. I need to adopt my metrics on an open source tool. I have found PMD and check style on sourceforge.net but there is not adequate explanation about their codes. However, I couldn't to find their source code to customize them. I will be appreciated, if you introduce one open source tool for java which I can customize it's code.

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• Software development metrics and reporting

  - by David M

  I've had some interesting conversations recently about software development metrics, in particular how they can be used in a reasonably large organisation to help development teams work better. I know there have been Stack Overflow questions about which metrics are good to use - like this one, but my question is more about which metrics are useful to which stakeholders, and at what level of aggregation. As an example, my view is that code coverage is a useful metric in the following ways (and maybe others): For a team's own internal use when combined with other measurements. For facilitating/enabling/mentoring teams, where it might be instructive when considered on a team-by-team basis as a trend (e.g. if team A and B have coverage this month of 75 and 50, I'd be more concerned with team A than B if the previous month they'd had 80 and 40). For senior management when presented as an aggregated statistic across a number of teams or a whole department. But I don't think it's useful for senior management to see this on a team-by-team basis, as this encourages artifical attempts to bolster coverage with tests that merely exercise, rather than test, code. I'm in an organisation with a couple of levels in its management hierarchy, but where the vast majority of managers are technically minded and able (with many still getting their hands dirty). Some of the development teams are leading the way in driving towards agile development practices, but others lag, and there is now a serious mandate from the top for this to be the way the organisation works. A couple of us are starting a programme to encourage this. In this sort of an organisation, what sort of metrics do you think are useful, to whom, why, and at what level of aggregation? I don't want people to feel their performance is being assessed based on a metric that they can artificially influence; at the same time, the senior management are going to want some sort of evidence that progress is being made. What advice or caveats can you provide based on experience in your own organisations? EDIT We are definitely wanting to use metrics as a tool for organisational improvement not as a tool for individual performance measurement.

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• program for calculating metrics in java

  - by senzacionale

  i need to calculate few metrics (CBO, NOC, DAC, LCOM, WMC, RFC and DIT metric). Program is written in jdeveloper and i do not know how to calculate this metrics. Migration to eclipse is not possible becouse code is not compiled. Does anyone know any good program for calculating metrics?

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• metrics in sonar

  - by senzacionale

  http://docs.codehaus.org/display/SONAR/Metric+definitions how can i use metrics in sonar? Is there any plugin for all this metrics? here are plugins but not for all metrics: http://docs.codehaus.org/display/SONAR/Sonar+Plugin+Library/

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• Metrics - A little knowledge can be a dangerous thing (or 'Why you're not clever enough to interpret metrics data')

  - by Jason Crease

  At RedGate Software, I work on a .NET obfuscator  called SmartAssembly.  Various features of it use a database to store various things (exception reports, name-mappings, etc.) The user is given the option of using either a SQL-Server database (which requires them to have Microsoft SQL Server), or a Microsoft Access MDB file (which requires nothing). MDB is the default option, but power-users soon switch to using a SQL Server database because it offers better performance and data-sharing. In the fashionable spirit of optimization and metrics, an obvious product-management question is 'Which is the most popular? SQL Server or MDB?' We've collected data about this fact, using our 'Feature-Usage-Reporting' technology (available as part of SmartAssembly) and more recently our 'Application Metrics' technology: Parameter Number of users % of total users Number of sessions Number of usages SQL Server 28 19.0 8115 8115 MDB 114 77.6 1449 1449 (As a disclaimer, please note than SmartAssembly has far more than 132 users . This data is just a selection of one build) So, it would appear that SQL-Server is used by fewer users, but more often. Great. But here's why these numbers are useless to me: Only the original developers understand the data What does a single 'usage' of 'MDB' mean? Does this happen once per run? Once per option change? On clicking the 'Obfuscate Now' button? When running the command-line version or just from the UI version? Each question could skew the data 10-fold either way, and the answers only known by the developer that instrumented the application in the first place. In other words, only the original developer can interpret the data - product-managers cannot interpret the data unaided. Most of the data is from uninterested users About half of people who download and run a free-trial from the internet quit it almost immediately. Only a small fraction use it sufficiently to make informed choices. Since the MDB option is the default one, we don't know how many of those 114 were people CHOOSING to use the MDB, or how many were JUST HAPPENING to use this MDB default for their 20-second trial. This is a problem we see across all our metrics: Are people are using X because it's the default or are they using X because they want to use X? We need to segment the data further - asking what percentage of each percentage meet our criteria for an 'established user' or 'informed user'. You end up spending hours writing sophisticated and dubious SQL queries to segment the data further. Not fun. You can't find out why they used this feature Metrics can answer the when and what, but not the why. Why did people use feature X? If you're anything like me, you often click on random buttons in unfamiliar applications just to explore the feature-set. If we listened uncritically to metrics at RedGate, we would eliminate the most-important and more-complex features which people actually buy the software for, leaving just big buttons on the main page and the About-Box. "Ah, that's interesting!" rather than "Ah, that's actionable!" People do love data. Did you know you eat 1201 chickens in a lifetime? But just 4 cows? Interesting, but useless. Often metrics give you a nice number: '5.8% of users have 3 or more monitors' . But unless the statistic is both SUPRISING and ACTIONABLE, it's useless. Most metrics are collected, reviewed with lots of cooing. and then forgotten. Unless a piece-of-data could change things, it's useless collecting it. People get obsessed with significance levels The first things that lots of people do with this data is do a t-test to get a significance level ("Hey! We know with 99.64% confidence that people prefer SQL Server to MDBs!") Believe me: other causes of error/misinterpretation in your data are FAR more significant than your t-test could ever comprehend. Confirmation bias prevents objectivity If the data appears to match our instinct, we feel satisfied and move on. If it doesn't, we suspect the data and dig deeper, plummeting down a rabbit-hole of segmentation and filtering until we give-up and move-on. Data is only useful if it can change our preconceptions. Do you trust this dodgy data more than your own understanding, knowledge and intelligence?  I don't. There's always multiple plausible ways to interpret/action any data Let's say we segment the above data, and get this data: Post-trial users (i.e. those using a paid version after the 14-day free-trial is over): Parameter Number of users % of total users Number of sessions Number of usages SQL Server 13 9.0 1115 1115 MDB 5 4.2 449 449 Trial users: Parameter Number of users % of total users Number of sessions Number of usages SQL Server 15 10.0 7000 7000 MDB 114 77.6 1000 1000 How do you interpret this data? It's one of: Mostly SQL Server users buy our software. People who can't afford SQL Server tend to be unable to afford or unwilling to buy our software. Therefore, ditch MDB-support. Our MDB support is so poor and buggy that our massive MDB user-base doesn't buy it.  Therefore, spend loads of money improving it, and think about ditching SQL-Server support. People 'graduate' naturally from MDB to SQL Server as they use the software more. Things are fine the way they are. We're marketing the tool wrong. The large number of MDB users represent uninformed downloaders. Tell marketing to aggressively target SQL Server users. To choose an interpretation you need to segment again. And again. And again, and again. Opting-out is correlated with feature-usage Metrics tends to be opt-in. This skews the data even further. Between 5% and 30% of people choose to opt-in to metrics (often called 'customer improvement program' or something like that). Casual trial-users who are uninterested in your product or company are less likely to opt-in. This group is probably also likely to be MDB users. How much does this skew your data by? Who knows? It's not all doom and gloom. There are some things metrics can answer well. Environment facts. How many people have 3 monitors? Have Windows 7? Have .NET 4 installed? Have Japanese Windows? Minor optimizations.  Is the text-box big enough for average user-input? Performance data. How long does our app take to start? How many databases does the average user have on their server? As you can see, questions about who-the-user-is rather than what-the-user-does are easier to answer and action. Conclusion Use SmartAssembly. If not for the metrics (called 'Feature-Usage-Reporting'), then at least for the obfuscation/error-reporting. Data raises more questions than it answers. Questions about environment are the easiest to answer.

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• Code Measuring and Metrics Tools?

  - by David

  I'm in the process of setting up a build server for personal projects. This server will handle all the normal CI stuff, including running large suites of tests (unit, integration, automated UI). While I'm working out the kinks for including code coverage output with MSTest, it occurs to me that there may be lots of tools out there which give me additional metrics other than just code coverage. FxCop comes to mind as an example. Though I'm sure there are others. Anything that can generate useful reportable data and metrics would be good. Whether it's class dependency charts (looking for Law of Demeter violations, for example), analyses of the uses of classes/functions (looking for a function that isn't used in the system other than just the tests, for example), and so on. I'm not sure the right way to formulate the question, since polling questions or "What's your favorite code analysis tool" aren't very good. But I'm essentially just looking for recommendations on what metrics to gather and the tools that can gather them. The eventual vision for something like this is to have the CI server run a bunch of automated tests and analysis tools and track performance metrics over time. Imagine a dashboard full of graphs plotting these metrics over time. The lines should all relatively be at an equilibrium, and if one starts to stray toward the negative then it's an early indication of problems with the code. In the age old struggle to quantify code quality with management, this sounds like a potentially helpful means of doing just that.

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• A simple metrics system BMI calculator in Windows Forms

  A simple metrics system BMI calculator in Windows Forms

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• Visual Studio Code Metrics and the Maintainability index of switch case

  - by pee2002

  Hi there! As a person who loves to follow the best practices, If i run code metrics (right click on project name in solution explorer and select "Calculate Code Metrics" - Visual Studio 2010) on: public static string GetFormFactor(int number) { string formFactor = string.Empty; switch (number) { case 1: formFactor = "Other"; break; case 2: formFactor = "SIP"; break; case 3: formFactor = "DIP"; break; case 4: formFactor = "ZIP"; break; case 5: formFactor = "SOJ"; break; } return formFactor; } It Gives me a Maintainability index of 61 (of course this is insignificant if you have only this, but if you use an utility like class whos philosophy is doing stuff like that, your utility class will have the maintainability index much worst..) Whats the solution for this?

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• Visual Studio code metrics misreporting lines of code

  - by Ian Newson

  The code metrics analyser in Visual Studio, as well as the code metrics power tool, report the number of lines of code in the TestMethod method of the following code as 8. At the most, I would expect it to report lines of code as 3. [TestClass] public class UnitTest1 { private void Test(out string str) { str = null; } [TestMethod] public void TestMethod() { var mock = new Mock<UnitTest1>(); string str; mock.Verify(m => m.Test(out str)); } } Can anyone explain why this is the case? Further info After a little more digging I've found that removing the out parameter from the Test method and updating the test code causes LOC to be reported as 2, which I believe is correct. The addition of out causes the jump, so it's not because of braces or attributes. Decompiling the DLL with dotPeek reveals a fair amount of additional code generated because of the out parameter which could be considered 8 LOC, but removing the parameter and decompiling also reveals generated code, which could be considered 5 LOC, so it's not simply a matter of VS counting compiler generated code (which I don't believe it should do anyway).

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• How are certain analytics metrics (time on site, etc.) usually distributed?

  - by a barking spider

  I'm not sure if I've come to the right place to ask this question, but I'm gathering some information for a research project. We're trying to design an experiment that'll heavily involve web analytics, and I'm trying to figure out some sensible values of mean +/- standard deviation for the following visitor-level (i.e., visitor 1 spends 2 minutes on site, visitor 2 spends 1 minute -- mean 1.5 +/- 0.71...) metrics: time spent on site page views If time allowed, we would put up the sites and gather the information ourselves, but we have a grant deadline coming up. I realize that even though these the distributions of these quantities are probably going to be heavily skewed towards zero, we'll need some reasonable figures or estimates of these figures in order to do sample size calculations, etc. Anyway, I'm not sure where else I'd turn, and I certainly have had a difficult time finding these values in the prior literature. If someone could direct me to a paper with the right information, or if you have these figures on hand (perhaps taken directly from your logs!) -- that would be amazing, and I'd love to hear from you. Thanks in advance, and even though I'm not allowed to reveal too much, rest assured that this info'll be applied towards a good cause :)

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• What are the most useful software development metrics?

  - by kchad

  I would like to track metrics that can be used to improve my team’s software development process, improve time estimates, and detect special case variations that need to be addressed during the project execution. Please limit each answer to a single metric, describe how to use it, and vote up the good answers.

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