Virtual mutation analysis of relational database schemas

Abstract

Relational databases are a vital component of many modern software applications. Key to the definition of the database schemawhich specifies what types of data will be stored in the database and the structure in which the data is to be organized -are integrity constraints. Integrity constraints are conditions that protect and preserve the consistency and validity of data in the database, preventing data values that violate their rules from being admitted into database tables. They encode logic about the application concerned, and like any other component of a software application, need to be properly tested. Mutation analysis is a technique that has been successfully applied to integrity constraint testing, seeding database schema faults of both omission and commission. Yet, as for traditional mutation analysis for program testing, it is costly to perform, since the test suite under analysis needs to be run against each individual mutant to establish whether or not it exposes the fault. One overhead incurred by database schema mutation is the cost of communicating with the database management system (DBMS). In this paper, we seek to eliminate this cost by performing mutation analysis virtually on a local model of the DBMS, rather than on an actual, running instance hosting a real database. We present an empirical evaluation of our virtual technique revealing that, across all of the studied DBMSs and schemas, the virtual method yields an average time saving of 51% over the baseline.

DOMINO: Fast and Effective Test Data Generation for Relational Database Schemas

“…The Alternating Variable Method (AVM) [9], [15], [16] is a local search technique that has been used to successfully generate test data for programs [17], [18] and has been applied to the automated generation of data for the INSERT statements used when testing relational database schemas [7], [10]. The AVM works to optimize a vector of test values according to a fitness function.…”

SchemaAnalyst: Search-Based Test Data Generation for Relational Database Schemas

“…SchemaAnalyst has been used to support research studies focusing on both search-based software testing [7], [9], [10] and mutation testing [11], [12], [13], [14]. In addition to describing the design and implementation of SchemaAnalyst and overviewing its efficiency and effectiveness, this paper inaugurates the public release of this tool.…”

mrstudyr: Retrospectively Studying the Effectiveness of Mutant Reduction Techniques

“…As studying the mutant data retrospectively removes the need to comprehend the complexities of a target environment, mutant reduction methods can be extended to new domains such as that of relational database schemas [19], [20], [21]. Ensuring that a database's schema has correctly specified integrity constraints is important because these entities ensure that only valid data enters the database.…”

“…Ensuring that a database's schema has correctly specified integrity constraints is important because these entities ensure that only valid data enters the database. Even though there are 971,373 questions about databases on StackExchange, the technical question and answer website [22], little prior work has focused on testing these integrity constraints [19].…”

“…Since it is important to assess the quality of tests for relational database schemas, recent work has proposed and evaluated database-aware mutation analysis techniques [19], [20], [21]. Although the presented method and tool are general, this paper illustrates the retrospective study of mutant reducers and the use of mrstudyr in the area of mutation analysis for database schemas.…”