It is somewhat unfortunate that the term "theorem proving" looks so much like the word "theory." Most researchers and practitioners in software assume that mechanized theorem proving is profoundly impractical. Indeed, until recently, most advances in theorem proving for higher-order logics have been largely theoretical. However, starting around the beginning of the 21st century, there was a surge in the use of proof assistants in serious verification efforts. That line of work is still quite new, but I believe it is not too soon to distill some lessons on how to work effectively with large formal proofs. Thus, this chapter gives some tips for structuring and maintaining large Coq developments.

Ltac Anti-Patterns

In this book, I have been following an unusual style, where proofs are not considered finished until they are "fully automated," in a certain sense. Each such theorem is proved by a single tactic. Since Ltac is a Turing-complete programming language, it is not hard to squeeze arbitrary heuristics into single tactics, using operators like the semicolon to combine steps. In contrast, most Ltac proofs "in the wild" consist of many steps, performed by individual tactics followed by periods. Is it really worth drawing a distinction between proof steps terminated by semicolons and steps terminated by periods? I argue that this is, in fact, a very important distinction, with serious consequences for a majority of important verification domains. The more uninteresting drudge work a proof domain involves, the more important it is to work to prove theorems with single tactics. From an automation standpoint, single-tactic proofs can be extremely effective, and automation becomes more and more critical as proofs are populated by more uninteresting detail. In this section, I will give some examples of the consequences of more common proof styles. As a running example, consider a basic language of arithmetic expressions, an interpreter for it, and a transformation that scales up every constant in an expression.

We can write a very manual proof that times really implements multiplication.

We use spaces to separate the two inductive cases, but note that these spaces have no real semantic content; Coq does not enforce that our spacing matches the real case structure of a proof. The second case mentions automatically generated hypothesis names explicitly. As a result, innocuous changes to the theorem statement can invalidate the proof.

  rewrite IHe1.

Error: The reference IHe1 was not found in the current environment.

The inductive hypotheses are named IHx1 and IHx2 now, not IHe1 and IHe2.

We might decide to use a more explicit invocation of induction to give explicit binders for all of the names that we will reference later in the proof.

We pass induction an intro pattern, using a | character to separate instructions for the different inductive cases. Within a case, we write ? to ask Coq to generate a name automatically, and we write an explicit name to assign that name to the corresponding new variable. It is apparent that, to use intro patterns to avoid proof brittleness, one needs to keep track of the seemingly unimportant facts of the orders in which variables are introduced. Thus, the script keeps working if we replace e by x, but it has become more cluttered. Arguably, neither proof is particularly easy to follow. That category of complaint has to do with understanding proofs as static artifacts. As with programming in general, with serious projects, it tends to be much more important to be able to support evolution of proofs as specifications change. Unstructured proofs like the above examples can be very hard to update in concert with theorem statements. For instance, consider how the last proof script plays out when we modify times to introduce a bug.

  rewrite IHe1.

Error: The reference IHe1 was not found in the current environment.

Can you spot what went wrong, without stepping through the script step-by-step? The problem is that trivial never fails. Originally, trivial had been succeeding in proving an equality that follows by reflexivity. Our change to times leads to a case where that equality is no longer true. The invocation trivial happily leaves the false equality in place, and we continue on to the span of tactics intended for the second inductive case. Unfortunately, those tactics end up being applied to the first case instead. The problem with trivial could be "solved" by writing, e.g., solve [ trivial ] instead, so that an error is signaled early on if something unexpected happens. However, the root problem is that the syntax of a tactic invocation does not imply how many subgoals it produces. Much more confusing instances of this problem are possible. For example, if a lemma L is modified to take an extra hypothesis, then uses of apply L will generate more subgoals than before. Old unstructured proof scripts will become hopelessly jumbled, with tactics applied to inappropriate subgoals. Because of the lack of structure, there is usually relatively little to be gleaned from knowledge of the precise point in a proof script where an error is raised.

Many real developments try to make essentially unstructured proofs look structured by applying careful indentation conventions, idempotent case-marker tactics included solely to serve as documentation, and so on. All of these strategies suffer from the same kind of failure of abstraction that was just demonstrated. I like to say that if you find yourself caring about indentation in a proof script, it is a sign that the script is structured poorly. We can rewrite the current proof with a single tactic.

We use the form of the semicolon operator that allows a different tactic to be specified for each generated subgoal. This change improves the robustness of the script: we no longer need to worry about tactics from one case being applied to a different case. Still, the proof script is not especially readable. Probably most readers would not find it helpful in explaining why the theorem is true. The same could be said for scripts using the bullets or curly braces provided by Coq 8.4, which allow code like the above to be stepped through interactively, with periods in place of the semicolons, while representing proof structure in a way that is enforced by Coq. Interactive replay of scripts becomes easier, but readability is not really helped. The situation gets worse in considering extensions to the theorem we want to prove. Let us add multiplication nodes to our exp type and see how the proof fares.

  induction e as [ | ? IHe1 ? IHe2 ]; [
    trivial
    | simpl; rewrite IHe1; rewrite IHe2; rewrite mult_plus_distr_l; trivial ].

Error: Expects a disjunctive pattern with 3 branches.

Unsurprisingly, the old proof fails, because it explicitly says that there are two inductive cases. To update the script, we must, at a minimum, remember the order in which the inductive cases are generated, so that we can insert the new case in the appropriate place. Even then, it will be painful to add the case, because we cannot walk through proof steps interactively when they occur inside an explicit set of cases.

Now we are in a position to see how much nicer is the style of proof that we have followed in most of this book.

This style is motivated by a hard truth: one person's manual proof script is almost always mostly inscrutable to most everyone else. I claim that step-by-step formal proofs are a poor way of conveying information. Thus, we might as well cut out the steps and automate as much as possible. What about the illustrative value of proofs? Most informal proofs are read to convey the big ideas of proofs. How can reading induction e; crush convey any big ideas? My position is that any ideas that standard automation can find are not very big after all, and the real big ideas should be expressed through lemmas that are added as hints. An example should help illustrate what I mean. Consider this function, which rewrites an expression using associativity of addition and multiplication.

One subgoal remains:
  IHe2 : eval e3 * eval e4 = eval e2
  ============================
   eval e1 * eval e3 * eval e4 = eval e1 * eval e2

The crush tactic does not know how to finish this goal. We could finish the proof manually.

However, the proof would be easier to understand and maintain if we separated this insight into a separate lemma.

In the limit, a complicated inductive proof might rely on one hint for each inductive case. The lemma for each hint could restate the associated case. Compared to manual proof scripts, we arrive at more readable results. Scripts no longer need to depend on the order in which cases are generated. The lemmas are easier to digest separately than are fragments of tactic code, since lemma statements include complete proof contexts. Such contexts can only be extracted from monolithic manual proofs by stepping through scripts interactively. The more common situation is that a large induction has several easy cases that automation makes short work of. In the remaining cases, automation performs some standard simplification. Among these cases, some may require quite involved proofs; such a case may deserve a hint lemma of its own, where the lemma statement may copy the simplified version of the case. Alternatively, the proof script for the main theorem may be extended with some automation code targeted at the specific case. Even such targeted scripting is more desirable than manual proving, because it may be read and understood without knowledge of a proof's hierarchical structure, case ordering, or name binding structure. A competing alternative to the common style of Coq tactics is the declarative style, most frequently associated today with the Isar language. A declarative proof script is very explicit about subgoal structure and introduction of local names, aiming for human readability. The coding of proof automation is taken to be outside the scope of the proof language, an assumption related to the idea that it is not worth building new automation for each serious theorem. I have shown in this book many examples of theorem-specific automation, which I believe is crucial for scaling to significant results. Declarative proof scripts make it easier to read scripts to modify them for theorem statement changes, but the alternate adaptive style from this book allows use of the same scripts for many versions of a theorem. Perhaps I am a pessimist for thinking that fully formal proofs will inevitably consist of details that are uninteresting to people, but it is my preference to focus on conveying proof-specific details through choice of lemmas. Additionally, adaptive Ltac scripts contain bits of automation that can be understood in isolation. For instance, in a big repeat match loop, each case can generally be digested separately, which is a big contrast from trying to understand the hierarchical structure of a script in a more common style. Adaptive scripts rely on variable binding, but generally only over very small scopes, whereas understanding a traditional script requires tracking the identities of local variables potentially across pages of code. One might also wonder why it makes sense to prove all theorems automatically (in the sense of adaptive proof scripts) but not construct all programs automatically. My view there is that program synthesis is a very useful idea that deserves broader application! In practice, there are difficult obstacles in the way of finding a program automatically from its specification. A typical specification is not exhaustive in its description of program properties. For instance, details of performance on particular machine architectures are often omitted. As a result, a synthesized program may be correct in some sense while suffering from deficiencies in other senses. Program synthesis research will continue to come up with ways of dealing with this problem, but the situation for theorem proving is fundamentally different. Following mathematical practice, the only property of a formal proof that we care about is which theorem it proves, and it is trivial to check this property automatically. In other words, with a simple criterion for what makes a proof acceptable, automatic search is straightforward. Of course, in practice we also care about understandability of proofs to facilitate long-term maintenance, which is just what motivates the techniques outlined above, and the next section gives some related advice.

Debugging and Maintaining Automation

Fully automated proofs are desirable because they open up possibilities for automatic adaptation to changes of specification. A well-engineered script within a narrow domain can survive many changes to the formulation of the problem it solves. Still, as we are working with higher-order logic, most theorems fall within no obvious decidable theories. It is inevitable that most long-lived automated proofs will need updating. Before we are ready to update our proofs, we need to write them in the first place. While fully automated scripts are most robust to changes of specification, it is hard to write every new proof directly in that form. Instead, it is useful to begin a theorem with exploratory proving and then gradually refine it into a suitable automated form. Consider this theorem from Chapter 8, which we begin by proving in a mostly manual way, invoking crush after each step to discharge any low-hanging fruit. Our manual effort involves choosing which expressions to case-analyze on.

In this complete proof, it is hard to avoid noticing a pattern. We rework the proof, abstracting over the patterns we find.

The expression we want to destruct here turns out to be the discriminee of a match, and we can easily enough write a tactic that destructs all such expressions.

This tactic invocation discharges the whole case. It does the same on the next two cases, but it gets stuck on the fourth case.

The subgoal's conclusion is:
    ============================
   (if expDenote e1 then expDenote (cfold e2) else expDenote (cfold e3)) =
   expDenote (if expDenote e1 then cfold e2 else cfold e3)

We need to expand our t tactic to handle this case.

Now the goal is discharged, but t' has no effect on the next subgoal.

A final revision of t finishes the proof.

We can take the final tactic and move it into the initial part of the proof script, arriving at a nicely automated proof.

Even after we put together nice automated proofs, we must deal with specification changes that can invalidate them. It is not generally possible to step through single-tactic proofs interactively. There is a command Debug On that lets us step through points in tactic execution, but the debugger tends to make counterintuitive choices of which points we would like to stop at, and per-point output is quite verbose, so most Coq users do not find this debugging mode very helpful. How are we to understand what has broken in a script that used to work? An example helps demonstrate a useful approach. Consider what would have happened in our proof of reassoc_correct if we had first added an unfortunate rewriting hint.

One subgoal remains:
  ============================
   eval e1 * (eval e3 + 1 - 1) * eval e4 = eval e1 * eval e2

The poorly chosen rewrite rule fired, changing the goal to a form where another hint no longer applies. Imagine that we are in the middle of a large development with many hints. How would we diagnose the problem? First, we might not be sure which case of the inductive proof has gone wrong. It is useful to separate out our automation procedure and apply it manually.

Since we see the subgoals before any simplification occurs, it is clear that we are looking at the case for constants. Our t makes short work of it.

The next subgoal, for addition, is also discharged without trouble.

The final subgoal is for multiplication, and it is here that we get stuck in the proof state summarized above.

What is t doing to get us to this point? The info command can help us answer this kind of question. (As of this writing, info is no longer functioning in the most recent Coq release, but I hope it returns.)

 == simpl in *; intuition; subst; autorewrite with core in *;
    simpl in *; intuition; subst; autorewrite with core in *;
    simpl in *; intuition; subst; destruct (reassoc e2).
    simpl in *; intuition.
    
    simpl in *; intuition.
    
    simpl in *; intuition; subst; autorewrite with core in *;
       refine (eq_ind_r
                 (fun n : nat =>
                  n * (eval e3 + 1 - 1) * eval e4 = eval e1 * eval e2) _ IHe1);
       autorewrite with core in *; simpl in *; intuition;
    subst; autorewrite with core in *; simpl in *;
    intuition; subst.
 

A detailed trace of t's execution appears. Since we are using the very general crush tactic, many of these steps have no effect and only occur as instances of a more general strategy. We can copy-and-paste the details to see where things go wrong.

We arbitrarily split the script into chunks. The first few seem not to do any harm.

The next step is revealed as the culprit, bringing us to the final unproved subgoal.

We can split the steps further to assign blame.

It was the final of these four tactics that made the rewrite. We can find out exactly what happened. The info command presents hierarchical views of proof steps, and we can zoom down to a lower level of detail by applying info to one of the steps that appeared in the original trace.

 == refine (eq_ind_r (fun n : nat => n = eval e1 * eval e2) _
              (confounder (reassoc e1) e3 e4)).
      

The way a rewrite is displayed is somewhat baroque, but we can see that theorem confounder is the final culprit. At this point, we could remove that hint, prove an alternate version of the key lemma rewr, or come up with some other remedy. Fixing this kind of problem tends to be relatively easy once the problem is revealed.

Sometimes a change to a development has undesirable performance consequences, even if it does not prevent any old proof scripts from completing. If the performance consequences are severe enough, the proof scripts can be considered broken for practical purposes. Here is one example of a performance surprise.

The central element of the problem is the addition of transitivity as a hint. With transitivity available, it is easy for proof search to wind up exploring exponential search spaces. We also add a few other arbitrary variables and hypotheses, designed to lead to trouble later.

We prove a simple lemma very quickly, using the Time command to measure exactly how quickly.

Finished transaction in 0. secs (0.068004u,0.s)

Now we add a different hypothesis, which is innocent enough; in fact, it is even provable as a theorem.

Finished transaction in 2. secs (1.264079u,0.s)
      

Why has the search time gone up so much? The info command is not much help, since it only shows the result of search, not all of the paths that turned out to be worthless.

 == intro x; intro y; intro H; intro H0; intro H4;
       simple eapply trans_eq.
    simple apply eq_refl.
    
    simple eapply trans_eq.
    simple apply eq_refl.
    
    simple eapply trans_eq.
    simple apply eq_refl.
    
    simple apply H1.
    eexact H.
    
    eexact H0.
    
    simple apply H2; eexact H4.
    

This output does not tell us why proof search takes so long, but it does provide a clue that would be useful if we had forgotten that we added transitivity as a hint. The eauto tactic is applying depth-first search, and the proof script where the real action is ends up buried inside a chain of pointless invocations of transitivity, where each invocation uses reflexivity to discharge one subgoal. Each increment to the depth argument to eauto adds another silly use of transitivity. This wasted proof effort only adds linear time overhead, as long as proof search never makes false steps. No false steps were made before we added the new hypothesis, but somehow the addition made possible a new faulty path. To understand which paths we enabled, we can use the debug command.

The output is a large proof tree. The beginning of the tree is enough to reveal what is happening:
1 depth=6
1.1 depth=6 intro
1.1.1 depth=6 intro
1.1.1.1 depth=6 intro
1.1.1.1.1 depth=6 intro
1.1.1.1.1.1 depth=6 intro
1.1.1.1.1.1.1 depth=5 apply H3
1.1.1.1.1.1.1.1 depth=4 eapply trans_eq
1.1.1.1.1.1.1.1.1 depth=4 apply eq_refl
1.1.1.1.1.1.1.1.1.1 depth=3 eapply trans_eq
1.1.1.1.1.1.1.1.1.1.1 depth=3 apply eq_refl
1.1.1.1.1.1.1.1.1.1.1.1 depth=2 eapply trans_eq
1.1.1.1.1.1.1.1.1.1.1.1.1 depth=2 apply eq_refl
1.1.1.1.1.1.1.1.1.1.1.1.1.1 depth=1 eapply trans_eq
1.1.1.1.1.1.1.1.1.1.1.1.1.1.1 depth=1 apply eq_refl
1.1.1.1.1.1.1.1.1.1.1.1.1.1.1.1 depth=0 eapply trans_eq
1.1.1.1.1.1.1.1.1.1.1.1.1.1.2 depth=1 apply sym_eq ; trivial
1.1.1.1.1.1.1.1.1.1.1.1.1.1.2.1 depth=0 eapply trans_eq
1.1.1.1.1.1.1.1.1.1.1.1.1.1.3 depth=0 eapply trans_eq
1.1.1.1.1.1.1.1.1.1.1.1.2 depth=2 apply sym_eq ; trivial
1.1.1.1.1.1.1.1.1.1.1.1.2.1 depth=1 eapply trans_eq
1.1.1.1.1.1.1.1.1.1.1.1.2.1.1 depth=1 apply eq_refl
1.1.1.1.1.1.1.1.1.1.1.1.2.1.1.1 depth=0 eapply trans_eq
1.1.1.1.1.1.1.1.1.1.1.1.2.1.2 depth=1 apply sym_eq ; trivial
1.1.1.1.1.1.1.1.1.1.1.1.2.1.2.1 depth=0 eapply trans_eq
1.1.1.1.1.1.1.1.1.1.1.1.2.1.3 depth=0 eapply trans_eq

The first choice eauto makes is to apply H3, since H3 has the fewest hypotheses of all of the hypotheses and hints that match. However, it turns out that the single hypothesis generated is unprovable. That does not stop eauto from trying to prove it with an exponentially sized tree of applications of transitivity, reflexivity, and symmetry of equality. It is the children of the initial apply H3 that account for all of the noticeable time in proof execution. In a more realistic development, we might use this output of debug to realize that adding transitivity as a hint was a bad idea.

As aggravating as the above situation may be, there is greater aggravation to be had from importing library modules with commands like Require Import. Such a command imports not just the Gallina terms from a module, but also all the hints for auto, eauto, and autorewrite. Some very recent versions of Coq include mechanisms for removing hints from databases, but the proper solution is to be very conservative in exporting hints from modules. Consider putting hints in named databases, so that they may be used only when called upon explicitly, as demonstrated in Chapter 13. It is also easy to end up with a proof script that uses too much memory. As tactics run, they avoid generating proof terms, since serious proof search will consider many possible avenues, and we do not want to build proof terms for subproofs that end up unused. Instead, tactic execution maintains thunks (suspended computations, represented with closures), such that a tactic's proof-producing thunk is only executed when we run Qed. These thunks can use up large amounts of space, such that a proof script exhausts available memory, even when we know that we could have used much less memory by forcing some thunks earlier. The abstract tactical helps us force thunks by proving some subgoals as their own lemmas. For instance, a proof induction x; crush can in many cases be made to use significantly less peak memory by changing it to induction x; abstract crush. The main limitation of abstract is that it can only be applied to subgoals that are proved completely, with no undetermined unification variables in their initial states. Still, many large automated proofs can realize vast memory savings via abstract.

Modules

Last chapter's examples of proof by reflection demonstrate opportunities for implementing abstract proof strategies with stronger formal guarantees than can be had with Ltac scripting. Coq's module system provides another tool for more rigorous development of generic theorems. This feature is inspired by the module systems found in Standard ML and OCaml, and the discussion that follows assumes familiarity with the basics of one of those systems. ML modules facilitate the grouping of abstract types with operations over those types. Moreover, there is support for functors, which are functions from modules to modules. A canonical example of a functor is one that builds a data structure implementation from a module that describes a domain of keys and its associated comparison operations. When we add modules to a base language with dependent types, it becomes possible to use modules and functors to formalize kinds of reasoning that are common in algebra. For instance, the following module signature captures the essence of the algebraic structure known as a group. A group consists of a carrier set G, an associative binary operation f, a left identity element id for f, and an operation i that is a left inverse for f.

Many useful theorems hold of arbitrary groups. We capture some such theorem statements in another module signature.

We implement generic proofs of these theorems with a functor, whose input is an arbitrary group M.

As in ML, Coq provides multiple options for ascribing signatures to modules. Here we use just the colon operator, which implements opaque ascription, hiding all details of the module not exposed by the signature. Another option is transparent ascription via the <: operator, which checks for signature compatibility without hiding implementation details. Here we stick with opaque ascription but employ the with operation to add more detail to a signature, exposing just those implementation details that we need to. For instance, here we expose the underlying group representation set and operator definitions. Without such a refinement, we would get an output module proving theorems about some unknown group, which is not very useful. Also note that opaque ascription can in Coq have some undesirable consequences without analogues in ML, since not just the types but also the definitions of identifiers have significance in type checking and theorem proving.

To ensure that the module we are building meets the GROUP_THEOREMS signature, we add an extra local name for M, the functor argument.

It would be inconvenient to repeat the prefix M. everywhere in our theorem statements and proofs, so we bring all the identifiers of M into the local scope unqualified. Now we are ready to prove the three theorems. The proofs are completely manual, which may seem ironic given the content of the previous sections! This illustrates another lesson, which is that short proof scripts that change infrequently may be worth leaving unautomated. It would take some effort to build suitable generic automation for these theorems about groups, so I stick with manual proof scripts to avoid distracting us from the main message of the section. We take the proofs from the Wikipedia page on elementary group theory.

We can show that the integers with + form a group.

Next, we can produce integer-specific versions of the generic group theorems.

  IntProofs.unique_ident
     : forall e' : Int.G, (forall a : Int.G, Int.f e' a = a) -> e' = Int.e

Projections like Int.G are known to be definitionally equal to the concrete values we have assigned to them, so the above theorem yields as a trivial corollary the following more natural restatement:

As in ML, the module system provides an effective way to structure large developments. Unlike in ML, Coq modules add no expressiveness; we can implement any module as an inhabitant of a dependent record type. It is the second-class nature of modules that makes them easier to use than dependent records in many cases. Because modules may only be used in quite restricted ways, it is easier to support convenient module coding through special commands and editing modes, as the above example demonstrates. An isomorphic implementation with records would have suffered from lack of such conveniences as module subtyping and importation of the fields of a module. On the other hand, all module values must be determined statically, so modules may not be computed, e.g., within the definitions of normal functions, based on particular function parameters.

Build Processes

As in software development, large Coq projects are much more manageable when split across multiple files and when decomposed into libraries. Coq and Proof General provide very good support for these activities. Consider a library that we will name Lib, housed in directory LIB and split between files A.v, B.v, and C.v. A simple Makefile will compile the library, relying on the standard Coq tool coq_makefile to do the hard work.

MODULES := A B C
VS      := $(MODULES:%=%.v)

.PHONY: coq clean

coq: Makefile.coq
        $(MAKE) -f Makefile.coq

Makefile.coq: Makefile $(VS)
        coq_makefile -R . Lib $(VS) -o Makefile.coq

clean:: Makefile.coq
        $(MAKE) -f Makefile.coq clean
        rm -f Makefile.coq

The Makefile begins by defining a variable VS holding the list of filenames to be included in the project. The primary target is coq, which depends on the construction of an auxiliary Makefile called Makefile.coq. Another rule explains how to build that file. We call coq_makefile, using the -R flag to specify that files in the current directory should be considered to belong to the library Lib. This Makefile will build a compiled version of each module, such that X.v is compiled into X.vo. Now code in B.v may refer to definitions in A.v after running
Require Import Lib.A.
   

Library Lib is presented as a module, containing a submodule A, which contains the definitions from A.v. These are genuine modules in the sense of Coq's module system, and they may be passed to functors and so on. The command Require Import is a convenient combination of two more primitive commands. The Require command finds the .vo file containing the named module, ensuring that the module is loaded into memory. The Import command loads all top-level definitions of the named module into the current namespace, and it may be used with local modules that do not have corresponding .vo files. Another command, Load, is for inserting the contents of a named file verbatim. It is generally better to use the module-based commands, since they avoid rerunning proof scripts, and they facilitate reorganization of directory structure without the need to change code. Now we would like to use our library from a different development, called Client and found in directory CLIENT, which has its own Makefile.

MODULES := D E
VS      := $(MODULES:%=%.v)

.PHONY: coq clean

coq: Makefile.coq
        $(MAKE) -f Makefile.coq

Makefile.coq: Makefile $(VS)
        coq_makefile -R LIB Lib -R . Client $(VS) -o Makefile.coq

clean:: Makefile.coq
        $(MAKE) -f Makefile.coq clean
        rm -f Makefile.coq

We change the coq_makefile call to indicate where the library Lib is found. Now D.v and E.v can refer to definitions from Lib module A after running
Require Import Lib.A.
   

and E.v can refer to definitions from D.v by running
Require Import Client.D.
   

It can be useful to split a library into several files, but it is also inconvenient for client code to import library modules individually. We can get the best of both worlds by, for example, adding an extra source file Lib.v to Lib's directory and Makefile, where that file contains just this line:
Require Export Lib.A Lib.B Lib.C.
   

Now client code can import all definitions from all of Lib's modules simply by running
Require Import Lib.
   

The two Makefiles above share a lot of code, so, in practice, it is useful to define a common Makefile that is included by multiple library-specific Makefiles. The remaining ingredient is the proper way of editing library code files in Proof General. Recall this snippet of .emacs code from Chapter 2, which tells Proof General where to find the library associated with this book.

(custom-set-variables
  ...
  '(coq-prog-args '("-I" "/path/to/cpdt/src"))
  ...
)

To do interactive editing of our current example, we just need to change the flags to point to the right places.

(custom-set-variables
  ...
; '(coq-prog-args '("-I" "/path/to/cpdt/src"))
  '(coq-prog-args '("-R" "LIB" "Lib" "-R" "CLIENT" "Client"))
  ...
)

When working on multiple projects, it is useful to leave multiple versions of this setting in your .emacs file, commenting out all but one of them at any moment in time. To switch between projects, change the commenting structure and restart Emacs. Alternatively, we can revisit the directory-local settings approach and write the following into a file .dir-locals.el in CLIENT:

((coq-mode . ((coq-prog-args .
  ("-emacs-U" "-R" "LIB" "Lib" "-R" "CLIENT" "Client")))))

A downside of this approach is that users of your code may not want to trust the arbitrary Emacs Lisp programs that you are allowed to place in such files, so that they prefer to add mappings manually.