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It took me time T to develop program A, which is measured at 1000 lines of code (SLOC), in certain a language and domain and of a certain complexity. Is there a method to determine how much time it will take to develop program B, which is estimated to be 4000 lines, that has the same level of complexity, is in the same domain, and is developed in the same programming language?

I expect that the time it takes me will be grater than 4T. Is there a formula to estimate how T grows as SLOC count grows?

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It will take you 50% longer than you expect, even if you take this 50% into account. –  JUST MY correct OPINION Feb 5 '11 at 8:05
As for your second question, if you can solve that (make code bug-free before it hits QA), bottle it and make yourself richer than a dozen Bill Gates. –  JUST MY correct OPINION Feb 5 '11 at 8:06
At our company, we use 3 dice. –  The Scrum Meister Feb 5 '11 at 8:07
O(N^2) is overestimation. It might be n^alpha where alpha is > 1 and < 2. –  Andrei Feb 5 '11 at 8:10
I would think a good theoretical questimate of the form is difficulty equals AN +BN^2. The first term covers the cost to add a single line of code, without having to be concerned with interaction. The B terms covers interactions. So for small N, I would expect linear scaling, but once the code becomes large enough the interactions (side effects) dominate. Your postulated size may be too small for the quadratic term to be important. But, then I suspect software engineering can effect the exponent, as well as the constants A and B. –  Omega Centauri Feb 6 '11 at 4:24

7 Answers 7

up vote 5 down vote accepted

People have developed a number of models to try to estimate things like this. While I wouldn't try to claim that any of them is anywhere close to entirely reliable or accurate, there are a few that seem to take enough factors into account to give halfway reasonable estimates.

Just for one example, Barry Boehm's COCOMO II model seems to fit your situation reasonably well. According to one online implementation, your original 1 KLOC should have taken around 4 person months of effort, and your 4 KLOC should take around 10 person months (for one set of assumptions -- feel free to plug in more appropriate values for the type of development and such).

At the same time, I'd have to agree with others who've pointed out that lines of code is rarely a very good measure (of much of anything). Estimation based on function points (for one possibility) seems rather more accurate to me. Even at best, however, it will take substantially more work, and it may be open to question whether it produces results enough more accurate or reliable to justify that work, especially for a fairly small project like this.

Edit: Oops -- I pasted in the wrong link (that was for the original COCOMO model, not COCOMO II). COCOMO II is a bit more work to use (it might take a minute or two instead of 30 seconds), but produces (what are supposed to be) more accurate results. Online implementations are available. It definitely attempts to take more factors into account in any case (e.g., whether you can re-use any/all of the existing 1000 lines of code in the new project).

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Function points also have their limitations. They are useful for data-intensive projects without complex algorithms. The danger can be that you count "output price of lottery ticket" as one function point and "output tomorrow's winning lottery number" as equally complex, at just one function point. –  MarkJ Mar 15 '12 at 6:44

Applications can't be quantified in terms of LOC - it just doesn't work. Ever. So please, save yourself the hassle and don't do it.

Edit: Unless this is some sort of homework question... in which case the professor is a twit and you should go to a better school - n^2

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While ALL applications can't be quantified in terms of SLOC, for any given organization, their bread-and-butter apps most certainly can. Read "Software Engineering Economics", by Barry Boehm - and consider the fact that General Dynamics Fort Worth Division used to bet the company bottom line on a COCOMO estimator when they bid F-16C/D software development tasks to the Air Force. (I was there. I occasionally wrote such bids. They'd gone to a lot of trouble to collect the data and calbibrate their estimator.) –  John R. Strohm Feb 5 '11 at 23:32
That book is dated 1981, when popular languages included FORTRAN, ADA, Assembler and many others that had limited constructs that could (or should) be put into one line, so #LOC was likely proportional to effort and/or binary size. I've not read the book, but with tools like jsbeautifier.com and 'indent' around now, the total number of lines in a project is of no particular relevance unless used to compare to similar/previous projects by the same team. (amazon.com/dp/0138221227/?tag=stackoverfl08-20) –  JBRWilkinson Feb 8 '11 at 10:47

This is a bit controversial, but for project management SLOC is typically used for determining what the estimated timelines (i.e. read Software Estimation: Demystifying the Black Art (Best Practices (Microsoft))); however, what is usually underlined time and time again is that you need a large enough data set of similar problems you can start to notice trends in how fast it takes to develop things. Note that this also generally applies to very large code bases as well and you don't start to see accurate estimates until you are in the 100,000+ SLOC.

To build on MainMa's diving analogy a bit, if you are driving in a major city and all of the trips are less than 50 km you might eventually be able to say with a degree of confidence that the trip will take about 30 minutes under normal traffic conditions but the range of an individual trip might take between 15 minutes and two hours for any given instance.

This is similar to trying to estimate how long it will take to write a given function or story point since not all are the same. Resolving a story point that only involves getting some data and converting it to a report might only take a couple of hours for someone familiar with the project where as trying to improve upon some underlying queuing code your program is using might take several days. This is generally where evidenced-based scheduling is better as the developer is the one driving the estimate based upon their experience with the given task and then you adjust things based upon the historical evidence that relates to the developer which is why this technique tends to be better for task estimation.

Going back to the SLOC's as noted before, they can be used for estimating when a major project will be completed but only at the large scale and then don't scale down very well and require historical evidence of similar projects under similar conditions to generate the time-line estimate and they are really only used as guidance at the end of the day. Going back to the diving analogy. This is similar to long haul road trips (i.e. starting at 1,500 km) since the sheer amount of distance ensure that even though you might run into parts of the trip where you are crawling through traffic, you will also encounter times where you can go the speed limit for an extended period of time. This means that after you have done the trip a couple of times you can give a pretty reasonable estimate as to how fast you were averaging during the trip and how long it will take to get from point A to point B. Large projects are the same way: the sheer size of the project allow for project planners to be able to say that, "We have done a project of similar scope before in the past, it will likely be as big as those projects so the time to complete it will likely be similar to them."

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If you want your code to have less bugs, you should write a lot of automated tests, and do it before and while you write the code, and not after a component is ready. There are testing frameworks for different languages and platforms. You can read about Test Driven Development, there are a lot of online and offline resources on the subject.

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One really good reason to write tests before/during: If you don't then "it'll just work" and nobody will bother to write tests because, well, "it works". But then a regression bug may (will, really) be introduced or an edge-case encountered and there will be no tests to catch it until it ships to a client. Oops. Of course it also speeds up development by really cutting down on the feedback-cycle between write-and-crash-and-burn-with-bug (just accept it: bugs and incorrect logic are an axiom of programming). The shorter this cycle, the less time is wasted :) –  pst Feb 5 '11 at 9:02

Time (T) required for development (of a program) is not only function of lines of code (SLOC). It's also function of quality (Q) (and probably n+1 more variables).

If Q is low, then T grows somewhat linearly with SLOC. (You just bang more lines of code, and it's more or less a physical activity).

When Q gets higher T starts to grow exponentially and gets ever closer to infinity. (It's very hard to write a totally bug free code of more than three SLOC).

So, I think, it's almost impossible to estimate T if only given SLOC. Maybe, if you are lucky you might hit in the range of +-1 order of magnitudes. Eg. you estimate 10 days, and it might take something between 1 and 100 days.

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Lines of communication is also a factor in that the more people that are working on the project, the more people you need to discuss changes with which slows done the project. –  rjzii Feb 5 '11 at 16:41

4K lines of simple code may take you 1/10th the time as 1K lines of complex code. And 4K lines of complex code may take you 40 times the time as 1K lines of simple code. The measure is meaningless.

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You can't simply look at LOC/SLOC by itself the way you are trying to. The only way you can use LOCs with some degree of success (and as a guideline, not as an infallible rule) from previous projects to estimate future project sizes is by having a decent number of projects with their SLOC, number of resources (developers) and time of completion accounted for. Then you can use that to extrapolate.

But to take just one project, one single project, specially one that is not that big (1K is fairly small), that's just too little data to use LOC metrics in any meaningful manner.

If this is a homework, your professor is a clueless dick btw.

However, if this is for real, and if you are really that pressed, you could use the following guidelines:


With SPILLOFF = 1 giving you a 30% chance of success (a 70% chance of failure), SPILLOFF = 1.5 giving you a 60% probability of success (a 40% chance of failure) and SPILLOFF = 2 giving you a 90% chance of success (a 10$ chance of failure.) The reason for using such estimates is that failures in completing software projects tend to exhibit an exponential distribution with respect to the allocated time per time allocate (or whatever other resource you choose to use.)

When you have consistent work within an organizations or when you work in similar environments, and technology (not just the language, but the technology) as well as processes are uniform, then you can do some estimations with some margins of errors based on prior projects. In such cases, you want to give more weight to the most recent projects.

Say, for the last n + 1 projects (say n + 1 = 5 or 10... notice, it's n + 1, not n), you could do the following but only if you carefully keep track of the number of people involved in a project, actual number of LOC, actual completion time, and estimated completion time as estimated prior to the start of the project.

SUM = 0
FOR i = 1 to n

  SUM = ( ACTUAL_COST(i) * RUNNOF(i) ) + SUM


ESTIMATE = ( ( (SUM / (n + 1) ) + LAST ) / 2 ) * SPILLOFF

With SPILLOF as defined previously.

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OMG - you're actually calculating work estimates based on LOC? How about the time it took to do the work last time, rather than the LOC of last time? –  JBRWilkinson Feb 6 '11 at 18:37
@JBRWilkinson - within certain constrains (as pointed in my post), this work to a certain precision. As for your question, calculating the work done last time and nothing else only works if the next project is of a similar scope and magnitude. Furthermore, work is a function of several variables, at least amount of time resources (man-hours and/or time of completion) and physical resources (typically people, and in extreme cases, CPU/hardware expenditure and electricity.) –  luis.espinal Feb 7 '11 at 0:57
@JBRWilkinson - con't - These resources only measure effort, but you cannot implicitly deduce the scope or size of the project. Even within projects written with the same language, similar amount of resources might be needed for projects of different "size". You have to measure the size of the project a-priori (ie. expected function points, requirements and/or expected LOCs to name a few) or after (total implemented FPs, requirements, LOCs, etc.) –  luis.espinal Feb 7 '11 at 1:02
@JBRWilkinson - con't - once you consistently and methodically (these two are the key) measure spent resources and deliverable sizes (and hopefully type), you can more or less estimate what it takes within your organization to implement a system of a certain size (and possibly type) - assuming you have a process that you repeat consistently. You have to refine your numbers every X number of projects to account for outliers, and it is not hard to set up tracking of this data if you work methodically. This is really no different from projects in other engineering disciplines. –  luis.espinal Feb 7 '11 at 1:08
@JBRWilkinson - I've personally witnessed these type of methods work well within some organizations, and fail miserably with others - a function of the quality of work within each organization. A good software shop, be it agile, iterative or even waterfall can make this work (they can make anything works anyways). If still think just using work measures alone can help with estimations, let me know how you account for project scope and size. And to whoever mod me down, would be nice to hear your methodology and experience. Check the works of Phillip G. Armour when you get a chance. –  luis.espinal Feb 7 '11 at 1:10

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