A4: Search


In this assignment and the next, you will develop a search engine for text documents. Your engine will crawl through a directory on a local disk looking for documents. When it finds them, it will index the words that appear in those documents. Then it will answer queries posed by users.

In A4, you will build an working but inefficient version of the search engine. In A5, you will upgrade the data structures involved to make the engine scale up to large collections of documents.

This assignment is more difficult than A3. On a similar assignment last year, the mean hours worked was 13.2, and the standard deviation was 6.6. Please get started right away and make steady progress each day. Please track the time that you spend. We will ask you to report it.

What you’ll do: Implement some functors, and use a testing tool called Bisect.


Table of contents:

Step 1: Form a CMS Partnership

We recommend that you have a partner, but it is not required. Having a partner is not needed to complete the assignment: it is definitely do-able by one person. Nonetheless, working with another person is useful because it gives you a chance to bounce ideas off another person, and to get their help with fixing faults in your code. See the discussion of “pair programming”, below, for more details.

→ If you do have a partner, you must read the Partner Policy. ←

The deadline to form a CMS partnership is Friday, October 4, at 11:59 pm. There will be a short grace period. Then the assignment will close for a few hours. On Saturday, October 5, we will re-open the assignment, but you will no longer be able to form new partnerships. Instead, you will have to email one of your section TAs, cc’ing your intended partner. The TA can then manually put the two of you into a CMS partnership. However, there will be a penalty for late partner formation: -5 points on Saturday and Sunday, and -20 points on Monday. That penalty applies to both partners (no exceptions). Starting on Tuesday at 12:01 am, no new partnerships may be formed. When we re-open the assignment on Saturday, it will be changed to have the usual Wednesday 5 pm deadline with the usual 48-hour automatic extension policy.

Why have this early partner formation deadline, and the penalties? It’s to make sure you are working with your partner on the entire assignment, as required by the Partner Policy, rather than joining forces part way through.

If you want to split up with your partner before the final assignment deadline, you must acknowledge their influence on your work in the Authors interface. You may form a new partership, subject to the deadlines and penalties stated above.

Pair Programming. If you have a partner, you should be pair programming with them. Pair programming is a specific way for two people to write code together, and for both of them to own the result. Please watch this video, which explains the driver and navigator model of pair programming:

If you’d optionally like to read more about the benefits of pair programming, Strengthening the Case for Pair Programming by Williams et al. (2000) is a good place to start. It includes this quote:

For years, programmers in industry have claimed that by working collaboratively, they have produced higher-quality software products in shorter amounts of time. But their evidence was anecdotal and subjective: “It works” or “It feels right.” To validate these claims, we have gathered quantitative evidence showing that pair programming—two programmers working side by side at one computer on the same design, algorithm, code, or test—does indeed improve software quality and reduce time to market. Additionally, student and professional programmers consistently find pair programming more enjoyable than working alone. Yet most who have not tried and tested pair programming reject the idea as a redundant, wasteful use of programming resources: “Why would I put two people on a job that just one can do? I can’t afford to do that!” But we have found, as Larry Constantine wrote, that “Two programmers in tandem is not redundancy; it’s a direct route to greater efficiency and better quality.”

Step 2: Get Started

Download the release code. There are many files, and you might need to read most of them before the assignment is over, but you don’t have to do so yet.

Create a new git repo for this assignment. Make sure the repo is private. Add the release code to your repo. Refer back to the instructions in A1 if you need help with that. Make sure you unzip and copy the files correctly, including hidden dotfiles, as described in the A1 handout.

If you are using a partner, grant them access to the repo:

In the release code, there is a makefile provided with the usual targets: build, test, check, finalcheck, zip, docs, and clean.

We are back to an autograded assignment, hence your submission must pass make check. Now that you’re used to working with .mli files, we have omitted the comments about what you are allowed to change. Any names and types that we provide in an interface may not be changed, but you may of course add new declarations and definitions. If you’re ever in doubt about whether a change is permitted or not, just run make check: it will tell you whether you have changed the interface in a prohibited way.

Step 3: Dictionaries

The most important data structure that your search engine will need is a dictionary—that is, a mapping from keys to values. In this assignment, we’ll keep it simple and implement a dictionary with an association list (which you can read about in the textbook). In A5, we’ll upgrade to a more efficient implementation.

First, read the interfaces in dictionary.mli. There are a few advanced OCaml features used in them that you might not recognize, so we briefly describe them here:

Second, read the interface in listDictionary.mli. All it does is include the Dictionary interface and declare a functor.

Next, complete the code in listDictionary.ml, following the specifications and comments provided in the starter code. Using test-driven development, also implement unit tests for ListDictionary in test.ml. We will not be assessing how many test cases you have until the excellent scope, below, so see there for details.

Be extra careful to use the comparison operator provided by the KeySig, not Stdlib.compare (nor the built-in comparisons <, <=, =, etc.), when comparing keys. Otherwise your dictionary will not process keys in the right order, and you will lose points.

The file exampleDictionary.ml contains an example of how to create a dictionary. That file is intended to be loaded in utop with #use, not to be compiled with ocamlbuild.

Note that you need to document and implement an abstraction function and any representation invariants. The documentation goes above the representation type. The implementations go in format and rep_ok, respectively:

You do not need to have OUnit tests for format or rep_ok. Indeed, it would be hard or perhaps even impossible to write such tests.

Step 4: Sets

Your search engine will also need a data structure for sets. In this assignment, we’ll use a dictionary to represent a set. The core idea of this representation is that the elements of the set are the keys in the dictionary. The values in the dictionary are thus irrelevant, so they might as well simply be (), the unit value. Although there is a bit of extra space overhead using this idea, because of storing the unit value, it nonetheless provides a way to turn any dictionary data structure into a set data structure. We will profit from that in A5, when we will automatically get an improvement in the performance of our set data structures by upgrading our dictionary data structures.

Use that idea to implement dictionarySet.ml, specifically the Make functor, following the specifications and comments provided in the starter code. Also implement unit tests for it in test.ml. You will want to carefully read the specifications in dictionarySet.mli.

This is the stopping point for a satisfactory solution.

Step 5: Index

Now that we have the data structures we need, it’s time to implement the search engine. We’ll start with indexing the words found in files.

Begin by carefully reading the specifications in engine.mli. Then open engine.ml and implement the functions index_of_dir, words, to_list, and format in Engine.Make. It is a functor that produces a search engine out of dictionaries and sets. The ListEngine module, which you don’t need to modify, uses that functor to create a search engine based on the association list dictionaries you created earlier. Implement tests for ListEngine in test.ml. We provide more instructions and hints, below. Please read all the instructions below before beginning.

Avoid removal. Don’t use the remove function from the Dictionary interface in your implementation of the search engine, neither in this step nor the next step of the handout. You won’t get points off if you use it, but, we’re warning you that using it in A4 could make your A5 implementation much harder.

Files. The prototypical kind of file you should have in mind is a book stored as an ASCII-encoded plain text file, such as this edition of Alice’s Adventures in Wonderland. It would be reasonable to assume that the individual lines of files are not overly long, but that there might be many lines in a file.

We have provided two test directories for you in the release code. One is alice, which contains just that story. The other is preamble, which contains a couple very short files.

As a source for other test files, we recommend Project Gutenberg. Project Gutenberg files are often encoded in UTF-8 instead of ASCII. On most Unix systems, including the 3110 VM, you can convert UTF-8 to ASCII with the following command:

iconv -f UTF-8 -t ASCII -c in.txt >out.txt

where in.txt is the name of the input UTF-8 file and out.txt is the name of the output ASCII file.

Words: For purposes of this assignment, we define a word as follows:

There will no doubt be some weird corner cases resulting from this definition of words. But we need a standard definition; this one is relatively simple for us all to use, and it gets many common cases right.

For example, given a file containing the following text:

"I would found an institution where
any person can find instruction in
any study." ---E. Cornell (b. 1807)

The words in that file would be: 1807, an, any, b, can, Cornell, E, find, found, I, in, institution, instruction, person, study, where, would.

Hint: there are 3755 words in Alice’s Adventures in Wonderland, and after converting them all to lowercase, only 3278 distinct words remain.

Paths and filenames:

Library hints:

The modules mentioned above are specifically permitted by this assignment even if they have side effects or are imperative. When dealing with I/O, side effects are unavoidable.

Step 6: Query

The queries that users pose will have one of two forms. Abstractly those two forms are as follows, in which the NOT clause is always optional:

For example, “AND (far, away)” would return all files that contain both the words “far” and “away”, whereas “AND (far, away), NOT (day, night)” would return all files that do contain both “far” and “away” but do not contain “day” nor “night”. Likewise, “OR (all, nothing)” would return any file that contains “all” or “nothing” (or both), and “OR (all, nothing), NOT (between)” would return any file that contains “all” or “nothing” (or both) but does not contain “between”.

Queries must be case insensitive, which is the behavior you expect from Google. That means queries for the words “far”, “Far”, and “FAR” should all return the same results. Likewise, a file containing “far”, “Far”, or “FAR” would be returned by any of those queries.

Implement and_not and or_not in engine.ml, and implement unit tests in test.ml.

This is the stopping point for a good solution. Your A4 grade will be based on what you complete by the time A4 is due. A5 will then ask you to add new functionality. Any functionality mentioned above that you leave incomplete in A4 will become part of the Satisfactory scope of A5. But, the excellent scope of A4 will not be part of A5. So you do not ever have to do the excellent scope of A4. It is perfectly fine to stop right here.

Step 7: Bisect

The textbook contains a tutorial on a tool called Bisect, which is a code coverage testing tool. Do that tutorial.

Now run make bisect on your solution. Open report/index.html and examine the code coverage you have achieved in listDictionary.ml, dictionarySet.ml, and engine.ml. It’s unlikely you are at 100%, and probably it’s impossible to achieve that. For example, unit tests are unlikely to ever exercise (all or any of) your format and rep_ok functions. But outside of those, look for any lines colored red by the Bisect report. Do your best to invent new unit tests that will cause those lines to be executed. Add those tests to test.ml.

How high do you need to get your coverage? In an ideal world you would cover every line that you know it’s possible to cover, or at least that is feasible to write unit tests to cover. With modest effort, the staff solution to this assignment was able to achieve 90-100% code coverage in those three files, excluding format and rep_ok implementations.

To exclude those, follow the instructions at the end of the textbook tutorial in the section titled “Ignoring uncoverable code” regarding BISECT-IGNORE comments. Depending on when you first read the tutorial, you might not have seen that section; it was added just before this assignment released.

You may not use BISECT-IGNORE to unfairly increase your code coverage percentage. If you do, there will be significant penalties and perhaps even an Academic Integrity case, because you are falsifying scientific data.

In limited cases beyond format and rep_ok it might be fair to use BISECT-IGNORE. For example, if there is some defensive code that checks a precondition and raises an exception if it is violated, and it turns out to be impossible or infeasible to write a unit test to trigger that exception, then you should add additional source code comments to explain why it is reasonable to ignore that code in the Bisect report.


Note that there is no testing component to the rubric. Rather, it is absorbed into the excellent scope. The graders will not attempt to run make test on your submission.

Efficiency Rubric. Most of the test cases we run on your search engine will involve relatively small directories. But 5 points will be reserved for running your search engine on larger directories of up to 1 MB. That entails a high enough number of words that you will want to be careful about tail recursion. Of course, since the data structures are lists, the running time will likely be rather slow. For reference, the staff solutions take about 2–3 minutes to index that size directory.

Code Quality Rubric. This will be assessed the same as in A2, including documentation. Graders will read your extracted public and private HTML documentation, not the original source code comments. The make docs command must succeed, or you will lose all points on the documentation. We’re going to grade the top-level components of a module, not the nested parts. For example, when we grade listDictionary.ml’s documentation we won’t click on Make. The same holds for dictionarySet.ml and Make. So you don’t have to worry about the fact that ocamldoc doesn’t include the specification comments for Dictionary as part of ListDictionary.Make’s HTML: we won’t penalize you for those being missing.

Excellent Scope Rubric. We’ll make listDictionary.ml and engine.ml worth 4 points each, and dictionarySet.ml worth 2 points. We’ll look at your code-coverage percentages per-file and round any that are at least 90% to up a full 100%. Then you’ll get a number of points that is your percent code coverage on a file times the number of points the file is worth, rounded to the nearest integer.


Make sure your NetIDs are in authors.mli, and set the hours_worked variable at the end of authors.ml. Note that variable is now must be a list. Order does not matter in it.

Run make zip to construct the ZIP file you need to submit on CMS. Our autograder needs to be able to find the files you submit inside that ZIP without any human assistance, so:

→ DO NOT use your operating system’s graphical file browser to construct the ZIP file. ←

Use only the make zip command we provide. Mal-constructed ZIP files will receive a penalty of 15 points because of the extra human processing they will require. If CMS says your ZIP file is too large, it’s probably because you did not use make zip to construct it; the file size limit in CMS is plenty large for properly constructed ZIP files.

If you look closely, you will notice that make zip excludes all test directories. That is intended behavior. We don’t want to clutter CMS with your large tests. So, please do not go back into your ZIP and manually add your test directories to it. The course staff is not going to try and run make test or make bisect on your submission, so it is fine for your tests to be omitted. As for the Bisect report, make zip automatically produces it and includes it in your submission. So again, it will be fine that your tests are omitted.

Ensure that your solution passes make finalcheck. Submit your zipfile on CMS. Double-check before the deadline that you have submitted the intended version of your file.

Congratulations! Your search is off to a good start.

Acknowledgement: Adapted from Prof. Greg Morrisett, Dean and Vice Provost of Cornell Tech.