Machine Learning is the study of methods for programming computers to learn. Computers are applied to a wide range of tasks, and for most of these it is relatively easy for programmers to design and implement the necessary software. However, there are many tasks for which this is difficult or impossible. These can be divided into four general categories. First, there are problems for which there exist no human experts. For example, in modern automated manufacturing facilities, there is a need to predict machine failures before they occur by analyzing sensor readings. Because the machines are new, there are no human experts who can be interviewed by a programmer to provide the knowledge necessary to build a computer system. A machine learning system can study recorded data and subsequent machine failures and learn prediction rules. Second, there are problems where human experts exist, but where they are unable to explain their expertise. This is the case in many perceptual tasks, such as speech recognition, hand-writing recognition, and natural language understanding. Virtually all humans exhibit expert-level abilities on these tasks, but none of them can describe the detailed steps that they follow as they perform them. Fortunately, humans can provide machines with examples of the inputs and correct outputs for these tasks, so machine learning algorithms can learn to map the inputs to the outputs. Third, there are problems where phenomena are changing rapidly. In finance, for example, people would like to predict the future behavior of the stock market, of consumer purchases, or of exchange rates. These behaviors change frequently, so that even if a programmer could construct a good predictive computer program, it would need to be rewritten frequently. A learning program can relieve the programmer of this burden by constantly modifying and tuning a set of learned prediction rules. Fourth, there are applications that need to be customized for each computer user separately. Consider, for example, a program to filter unwanted electronic mail messages. Different users will need different filters. It is unreasonable to expect each user to program his or her own rules, and it is infeasible to provide every user with a software engineer to keep the rules up-to-date. A machine learning system can learn which mail messages the user rejects and maintain the filtering rules automatically. Machine learning addresses many of the same research questions as the fields of statistics, data mining, and psychology, but with differences of emphasis. Statistics focuses on understanding the phenomena that have generated the data, often with the goal of testing different hypotheses about those phenomena. Data mining seeks to find patterns in the data that are understandable by people. Psychological studies of human learning aspire to understand the mechanisms underlying the various learning behaviors exhibited by people (concept learning, skill acquisition, strategy change, etc.).

Machine learning

Model checking of linear temporal logic (LTL) specifications with respect to pushdown systems has been shown to be a useful tool for analysis of programs with potentially recursive procedures. LTL, however, can specify only regular properties, and properties such as correctness of procedures with respect to pre and post conditions, that require matching of calls and returns, are not regular. We introduce a temporal logic of calls and returns (CaRet) for specification and algorithmic verification of correctness requirements of structured programs. The formulas of CaRet are interpreted over sequences of propositional valuations tagged with special symbols call and ret. Besides the standard global temporal modalities, CaRet admits the abstract-next operator that allows a path to jump from a call to the matching return. This operator can be used to specify a variety of non-regular properties such as partial and total correctness of program blocks with respect to pre and post conditions. The abstract versions of the other temporal modalities can be used to specify regular properties of local paths within a procedure that skip over calls to other procedures. CaRet also admits the caller modality that jumps to the most recent pending call, and such caller modalities allow specification of a variety of security properties that involve inspection of the call-stack. Even though verifying context-free properties of pushdown systems is undecidable, we show that model checking CaRet formulas against a pushdown model is decidable. We present a tableau construction that reduces our model checking problem to the emptiness problem for a Buchi pushdown system. The complexity of model checking CaRet formulas is the same as that of checking LTL formulas, namely, polynomial in the model and singly exponential in the size of the specification.

https://repository.upenn.edu/cgi/viewcontent.cgi?article=1186&context=cis_papers

A Temporal Logic of Nested Calls and Returns

Aspect] is a simple and practical aspect-oriented extension to Java With just a few new constructs, AspectJ provides support for modular implementation of a range of crosscutting concerns. In AspectJ's dynamic join point model, join points are well-defined points in the execution of the program; pointcuts are collections of join points; advice are special method-like constructs that can be attached to pointcuts; and aspects are modular units of crosscutting implementation, comprising pointcuts, advice, and ordinary Java member declarations. AspectJ code is compiled into standard Java bytecode. Simple extensions to existing Java development environments make it possible to browse the crosscutting structure of aspects in the same kind of way as one browses the inheritance structure of classes. Several examples show that AspectJ is powerful, and that programs written using it are easy to understand.

An overview of AspectJ

Program slicing is a decomposition technique that slides program components not relevant to a chosen computation, referred to as a slicing criterion. The remaining components form an executable program called a slice that computes a projection of the original programpsilas semantics. Using examples coupled with fundamental principles, a tutorial introduction to program slicing is presented. Then applications of program slicing are surveyed, ranging from its first use as a debugging technique to current applications in property verification using finite state models. Finally, a summary of research challenges for the slicing community is discussed.

Program slicing

In optimizing compilers, data structure choices directly influence the power and efficiency of practical program optimization. A poor choice of data structure can inhibit optimization or slow compilation to the point that advanced optimization features become undesirable. Recently, static single assignment form and the control dependence graph have been proposed to represent data flow and control flow properties of programs. Each of these previously unrelated techniques lends efficiency and power to a useful class of program optimizations. Although both of these structures are attractive, the difficulty of their construction and their potential size have discouraged their use. We present new algorithms that efficiently compute these data structures for arbitrary control flow graphs. The algorithms use {\em dominance frontiers}, a new concept that may have other applications. We also give analytical and experimental evidence that all of these data structures are usually linear in the size of the original program. This paper thus presents strong evidence that these structures can be of practical use in optimization.

/pdf/efficiently-computing-static-single-assignment-form-and-the-3anyy9zi2s.pdf

Efficiently computing static single assignment form and the control dependence graph

The notion of a program slice, originally introduced by Mark Weiser, is useful in program debugging, automatic parallelization, and program integration. A slice of a program is taken with respect to a program point p and a variable x; the slice consists of all statements of the program that might affect the value of x at point p. This paper concerns the problem of interprocedural slicing—generating a slice of an entire program, where the slice crosses the boundaries of procedure calls. To solve this problem, we introduce a new kind of graph to represent programs, called a system dependence graph, which extends previous dependence representations to incorporate collections of procedures (with procedure calls) rather than just monolithic programs. Our main result is an algorithm for interprocedural slicing that uses the new representation. (It should be noted that our work concerns a somewhat restricted kind of slice: rather than permitting a program to b e sliced with respect to program point p and an arbitrary variable, a slice must be taken with respect to a variable that is defined or used at p.)The chief difficulty in interprocedural slicing is correctly accounting for the calling context of a called procedure. To handle this problem, system dependence graphs include some data dependence edges that represent transitive dependences due to the effects of procedure calls, in addition to the conventional direct-dependence edges. These edges are constructed with the aid of an auxiliary structure that represents calling and parameter-linkage relationships. This structure takes the form of an attribute grammar. The step of computing the required transitive-dependence edges is reduced to the construction of the subordinate characteristic graphs for the grammar's nonterminals.

/pdf/interprocedural-slicing-using-dependence-graphs-2fk5z60hh9.pdf

Interprocedural slicing using dependence graphs

The paper shows how a large class of interprocedural dataflow-analysis problems can be solved precisely in polynomial time by transforming them into a special kind of graph-reachability problem. The only restrictions are that the set of dataflow facts must be a finite set, and that the dataflow functions must distribute over the confluence operator (either union or intersection). This class of probable problems includes—but is not limited to—the classical separable problems (also known as “gen/kill” or “bit-vector” problems)—e.g., reaching definitions, available expressions, and live variables. In addition, the class of problems that our techniques handle includes many non-separable problems, including truly-live variables, copy constant propagation, and possibly-uninitialized variables.Results are reported from a preliminary experimental study of C programs (for the problem of finding possibly-uninitialized variables).

/pdf/precise-interprocedural-dataflow-analysis-via-graph-2q6e8l7f4e.pdf

Precise interprocedural dataflow analysis via graph reachability

We present a family of abstract-interpretation algorithms that are capable of determining "shape invariants" of programs that perform destructive updating on dynamically allocated storage. The main idea is to represent the stores that can possibly arise during execution using three-valued logical structures.Questions about properties of stores can be answered by evaluating predicate-logic formulae using Kleene's semantics of three-valued logic: If a formula evaluates to true, then the formula holds in every store represented by the three-valued structure.If a formula evaluates to false, then the formula does not hold in any store represented by the three-valued structure.If a formula evaluates to unknown, then we do not know if this formula always holds, never holds, or sometimes holds and sometimes does not hold in the stores represented by the three-valued structure.Three-valued logical structures are thus a conservative representation of memory stores.The approach described is a parametric framework: It provides the basis for generating a family of shape-analysis algorithms by varying the vocabulary used in the three-valued logic.

/pdf/parametric-shape-analysis-via-3-valued-logic-2rspv20sei.pdf

Parametric shape analysis via 3-valued logic

Shape analysis concerns the problem of determining "shape invariants" for programs that perform destructive updating on dynamically allocated storage. This article presents a parametric framework for shape analysis that can be instantiated in different ways to create different shape-analysis algorithms that provide varying degrees of efficiency and precision. A key innovation of the work is that the stores that can possibly arise during execution are represented (conservatively) using 3-valued logical structures. The framework is instantiated in different ways by varying the predicates used in the 3-valued logic. The class of programs to which a given instantiation of the framework can be applied is not limited a priori (i.e., as in some work on shape analysis, to programs that manipulate only lists, trees, DAGS, etc.); each instantiation of the framework can be applied to any program, but may produce imprecise results (albeit conservative ones) due to the set of predicates employed.

/pdf/parametric-shape-analysis-via-3-valued-logic-4np6ys2g2u.pdf

Programs are not text; they are hierarchical compositions of computational structures and should be edited, executed, and debugged in an environment that consistently acknowledges and reinforces this viewpoint. The Cornell Program Synthesizer demands a structural perspective at all stages of program development. Its separate features are unified by a common foundation: a grammar for the programming language. Its full-screen derivation-tree editor and syntax-directed diagnostic interpreter combine to make the Synthesizer a powerful and responsive interactive programming tool.

Thomas Reps

Papers

Interprocedural slicing using dependence graphs

Precise interprocedural dataflow analysis via graph reachability

Parametric shape analysis via 3-valued logic

Parametric shape analysis via 3-valued logic

The Cornell program synthesizer: a syntax-directed programming environment