Localizing SQL faults in database applications

Clark, Sarah R.; Cobb, Jake; Kapfhammer, Gregory M.; Jones, James A.; Harrold, Mary Jean

doi:10.1109/ase.2011.6100056

Cited by 26 publications

(18 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…We plan to incorporate techniques that generate more readable and realistic data values [31], [32], [33], [34], thus helping humans understand test cases more easily [17], [35]. We will also integrate the tool with others that support software maintenance activities like regression testing [36] and fault localization [37]. Next, we will extend the tool so that it enables the testing of recently developed NoSQL systems.…”

Section: Discussionmentioning

confidence: 99%

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

McMinn

Wright

Kinneer

et al. 2016

2016 IEEE International Conference on Software Maintenance and Evolution (ICSME)

Self Cite

View full text Add to dashboard Cite

Abstract-Data stored in relational databases plays a vital role in many aspects of society. When this data is incorrect, the services that depend on it may be compromised. The database schema is the artefact responsible for maintaining the integrity of stored data. Because of its critical function, the proper testing of the database schema is a task of great importance. Employing a search-based approach to generate high-quality test data for database schemas, SchemaAnalyst is a tool that supports testing this key software component. This presented tool is extensible and includes both an evaluation framework for assessing the quality of the generated tests and full-featured documentation. In addition to describing the design and implementation of SchemaAnalyst and overviewing its efficiency and effectiveness, this paper coincides with the tool's public release, thereby enhancing practitioners' ability to test relational database schemas.

show abstract

Section: Discussionmentioning

confidence: 99%

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

McMinn

Wright

Kinneer

et al. 2016

2016 IEEE International Conference on Software Maintenance and Evolution (ICSME)

Self Cite

View full text Add to dashboard Cite

show abstract

“…In the fault-based category the existing works range from the development of sets of mutants for SQL queries [17], [18] or schemas [19], [20], [21] to the evaluation of the fault-detection effectiveness with tools [22], [23], [24], [25], fault-localization [26] and empirical studies [27]. Others are application specific, mainly with the goal of detecting SQL injection vulnerabilities [28], [29], [30] and preventing them [31].…”

Section: Test Coverage For Database Applicationsmentioning

confidence: 99%

Coverage-Aware Test Database Reduction

Tuya

Riva

Suárez-Cabal

et al. 2016

IIEEE Trans. Software Eng.

View full text Add to dashboard Cite

Abstract-Functional testing of applications that process the information stored in databases often requires a careful design of the test database. The larger the test database, the more difficult it is to develop and maintain tests as well as to load and reset the test data. This paper presents an approach to reduce a database with respect to a set of SQL queries and a coverage criterion. The reduction procedures search the rows in the initial database that contribute to the coverage in order to find a representative subset that satisfies the same coverage as the initial database. The approach is automated and efficiently executed against large databases and complex queries. The evaluation is carried out over two real life applications and a well-known database benchmark. The results show a very large degree of reduction as well as scalability in relation to the size of the initial database and the time needed to perform the reduction.

show abstract

“…It was reported that there are common failures in a dynamic Web application that are caused by the interaction and passing of data between the application and the database [2]. Many researchers developed automated fault localization methods for traditional, non-database applications [1], [4], [5] and for data-centric applications (i.e.…”

Section: Introductionmentioning

confidence: 99%

Database-Aware Fault Localization for Dynamic Web Applications

Nguyen

2013

2013 IEEE International Conference on Software Maintenance

View full text Add to dashboard Cite

Localizing and fixing software faults is an important maintenance task. In a dynamic Web application, localizing the faults is challenging due to its dynamic nature and the interactions between the application and databases. The faults could occur in the statements in the host program or inside the queries that are sent from the application to be executed in the database engines. This paper presents SQLook, a novel databaseaware fault localization method that is able to locate output faults in PHP statements of a dynamic Web application as well as in SQL queries. In SQLook, a PHP interpreter is instrumented to execute an SQL query and to monitor the evaluation of those SQL predicates to determine if they affect the output process of individual data records. It performs row-based slicing across PHP statements and SQL queries to record the entities that are involved in the output of each data row. Our empirical evaluation shows that SQLook can achieve higher accuracy than the stateof-the-art database-aware fault localization approach.

show abstract

Localizing SQL faults in database applications

Cited by 26 publications

References 17 publications

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

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

Coverage-Aware Test Database Reduction

Database-Aware Fault Localization for Dynamic Web Applications

Contact Info

Product

Resources

About