A survey of problematic database code fragments in software systems

Alshemaimri, Bader; Elmasri, Ramez; Alsahfi, Tariq; Almotairi, Mousa

doi:10.1002/eng2.12441

Cited by 3 publications

(1 citation statement)

References 21 publications

(21 reference statements)

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“…The denormalization method, known as accelerating the response time of the database by adding rows or columns that are not needed in the database system, is used instead of the OLAP system, which requires additional costs [20]. The denormalization method is a widely used technique to improve the performance of the database system [21][22][23][24]. On the other hand, NoSQL database systems, which have flexible schemas for modern applications and are specially designed for certain data models, have become widespread due to their practical use, ease of development, and performance [25,26].…”

Section: Related Workmentioning

confidence: 99%

A Novel Approach to Improve the Performance of the Database Storing Big Data with Time Information

Taşyürek

2022

Balkan Journal of Electrical and Computer Engineering

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Big data is defined as data sets that are too large and/or complex to be processed by classical data processing methods. Big data analysis is essential because it enables more competent business movements, more efficient operations, and higher profits by using the data of institutions and organizations. However, large datasets are difficult to analyze because they are produced quickly, require large storage areas in computer systems, and the diversity of their data. In this study, a new approach using the denormalization method is proposed to accelerate the response time of the database in database systems where large volumes of data containing historical information are stored. Denormalization is defined as the process of adding rows or columns that are not needed to increase the reading performance of the database to the database system that has been normalized. In the proposed approach in this study, a large-volume dataset consisting of real spatial data belonging to Kayseri Metropolitan Municipality, containing temporal information and having approximately 96,000,000 row records, was used. In the proposed approach, the response time of the query is accelerated by recording the time information as numbers to increase the query performance of large volumes of data recorded in date format due to the temporal query process. The performance of the proposed method is compared with the performance of the normalization method using actual data on Microsoft SQL Server and Oracle database systems. The method proposed in the experimental evaluations shows that it works approximately eight times faster. In addition, the experimental results showed that the proposed method improves query performance more than the normalization-based method as the data size increases.

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Section: Related Workmentioning

confidence: 99%

A Novel Approach to Improve the Performance of the Database Storing Big Data with Time Information

Taşyürek

2022

Balkan Journal of Electrical and Computer Engineering

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Evaluation platform for sustainable operation of stadiums integrating multidimensional data: Based on a multifunctional perspective

Tang

Fan

Zong

et al. 2023

Energy and Buildings

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An Empirical Study on the Characteristics of Database Access Bugs in Java Applications

Liu,

Mondal,

Chen

2024

ACM Trans. Softw. Eng. Methodol.

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Database-backed applications rely on the database access code to interact with the underlying database management systems (DBMSs). Although many prior studies aim at database access issues like SQL anti-patterns or SQL code smells, there is a lack of study of database access bugs during the maintenance of database-backed applications. In this paper, we empirically investigate 423 database access bugs collected from seven large-scale Java open source applications that use relational database management systems (e.g., MySQL or PostgreSQL). We study the characteristics (e.g., occurrence and root causes) of the bugs by manually examining the bug reports and commit histories. We find that the number of reported database and non-database access bugs share a similar trend but their modified files in bug fixing commits are different. Additionally, we generalize categories of the root causes of database access bugs, containing five main categories (SQL queries, Schema, API, Configuration, SQL query result) and 25 unique root causes. We find that the bugs pertaining to SQL queries, Schema, and API cover 84.2% of database access bugs across all studied applications. In particular, SQL queries bug (54%) and API bug (38.7%) are the most frequent issues when using JDBC and Hibernate, respectively. Finally, we provide a discussion on the implications of our findings for developers and researchers.

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A survey of problematic database code fragments in software systems

Cited by 3 publications

References 21 publications

A Novel Approach to Improve the Performance of the Database Storing Big Data with Time Information

A Novel Approach to Improve the Performance of the Database Storing Big Data with Time Information

Evaluation platform for sustainable operation of stadiums integrating multidimensional data: Based on a multifunctional perspective

An Empirical Study on the Characteristics of Database Access Bugs in Java Applications

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