String (computer science)

About: String (computer science) is a research topic. Over the lifetime, 19430 publications have been published within this topic receiving 333247 citations. The topic is also known as: str & s.

Automated repair of HTML generation errors in PHP applications using string constraint solving

Abstract: PHP web applications routinely generate invalid HTML. Modern browsers silently correct HTML errors, but sometimes malformed pages render inconsistently, cause browser crashes, or expose security vulnerabilities. Fixing errors in generated pages is usually straightforward, but repairing the generating PHP program can be much harder. We observe that malformed HTML is often produced by incorrect "constant prints", i.e., statements that print string literals, and present two tools for automatically repairing such HTML generation errors. PHPQuickFix repairs simple bugs by statically analyzing individual prints. PHPRepair handles more general repairs using a dynamic approach. Based on a test suite, the property that all tests should produce their expected output is encoded as a string constraint over variables representing constant prints. Solving this constraint describes how constant prints must be modified to make all tests pass. Both tools were implemented as an Eclipse plugin and evaluated on PHP programs containing hundreds of HTML generation errors, most of which our tools were able to repair automatically.

Using q-grams in a DBMS for Approximate String Processing.

Abstract: String data is ubiquitous, and its management has taken on particular importance in the past few years. Approximate queries are very important on string data. This is due, for example, to the prevalence of typographical errors in data, and multiple conventions for recording attributes such as name and address. Commercial databases do not support approximate string queries directly, and it is a challenge to implement this functionality efficiently with user-defined functions (UDFs). In this paper, we develop a technique for building approximate string processing capabilities on top of commercial databases by exploiting facilities already available in them. At the core, our technique relies on generating short substrings of length q, called q-grams, and processing them using standard methods available in the DBMS. The proposed technique enables various approximate string processing methods in a DBMS, for example approximate (sub)string selections and joins, and can even be used with a variety of possible edit distance functions. The approximate string match predicate, with a suitable edit distance threshold, can be mapped into a vanilla relational expression and optimized by conventional relational optimizers.

Learning Semantic String Transformations from Examples

Abstract: We address the problem of performing semantic transformations on strings, which may represent a variety of data types (or their combination) such as a column in a relational table, time, date, currency, etc. Unlike syntactic transformations, which are based on regular expressions and which interpret a string as a sequence of characters, semantic transformations additionally require exploiting the semantics of the data type represented by the string, which may be encoded as a database of relational tables. Manually performing such transformations on a large collection of strings is error prone and cumbersome, while programmatic solutions are beyond the skill-set of end-users. We present a programming by example technology that allows end-users to automate such repetitive tasks. We describe an expressive transformation language for semantic manipulation that combines table lookup operations and syntactic manipulations. We then present a synthesis algorithm that can learn all transformations in the language that are consistent with the user-provided set of input-output examples. We have implemented this technology as an add-in for the Microsoft Excel Spreadsheet system and have evaluated it successfully over several benchmarks picked from various Excel help-forums.

Approximate Boyer-Moore string matching

Abstract: The Boyer–Moore idea applied in exact string matching is generalized to approximate string matching. Two versions of the problem are considered. The k mismatches problem is to find all approximate occurrences of a pattern string (length m) in a text string (length n) with at most k mismatches. The generalized Boyer–Moore algorithm is shown (under a mild independence assumption) to solve the problem in expected time $O(kn({1 / {(m - k) + ({k / c})}}))$, where c is the size of the alphabet. A related algorithm is developed for the k differences problem, where the task is to find all approximate occurrences of a pattern in a text with $ \leqslant k$ differences (insertions, deletions, changes). Experimental evaluation of the algorithms is reported, showing that the new algorithms are often significantly faster than the old ones. Both algorithms are functionally equivalent with the Horspool version of the Boyer–Moore algorithm when $k = 0$.

NESL: A Nested Data-Parallel Language (Version 2.6)

Abstract: This report describes NESL, a strongly-typed, applicative, data-parallel language. NESL is intended to be used as a portable interface for programming a variety of parallel and vector supercomputers, and as a basis for teaching parallel algorithms. Parallelism is supplied through a simple set of data-parallel constructs based on sequences (ordered sets), including a mechanism for applying any function over the elements of a sequence in parallel and a rich set of parallel functions that manipulate sequences. NESL fully supports nested sequences and nested parallelism -- the ability to take a parallel function and apply it over multiple instances in parallel. Nested parallelism is important for implementing algorithms with complex and dynamically changing data structures, such as required in many graph and sparse matrix algorithms. NESL also provides a mechanism for calculating the asymptotic running time for a program on various parallel machine models, including the parallel random access machine (PRAM). This is useful for estimating running times of algorithms on actual machines and, when teaching algorithms, for supplying a close correspondence between the code and the theoretical complexity. This report defines NESL and describes several examples of algorithms coded in the language. The examples include algorithms for median finding, sorting, string searching, finding prime numbers, and finding a planar convex hull. NESL currently compiles to an intermediate language called Vcode, which runs on the Cray Y-MP, Connection Machine CM-2, and Encore Multimax. For many algorithms, the current implementation gives performance close to optimized machine-specific code for these machines. Note: This report is an updated version of CMU-CS-92-103, which described version 2.4 of the language. The most significant changes in version 2.6 are that it supports polymorphic types, has an ML-like syntax instead of a lisp-like syntax, and includes support for I/O.