Loup Meurice

Understanding database schema evolution

Database reverse engineering (DRE) has traditionally been carried out by considering three main information sources: (1) the database schema, (2) the stored data, and (3) the application programs. Not all of these information sources are always available, or of sufficient quality to inform the DRE process. For example, getting access to real-world data is often extremely problematic for information systems that maintain private data. In recent years, the analysis of the evolution history of software programs have gained an increasing role in reverse engineering in general, but comparatively little such research has been carried out in the context of database reverse engineering. The goal of this paper is to contribute to narrowing this gap and exploring the use of the database evolution history as an additional information source to aid database schema reverse engineering. We present a tool-supported method for analyzing the evolution history of legacy databases, and we report on a large-scale case study of reverse engineering a complex information system and curate it as a benchmark for future research efforts within the community. We present a tool-supported method to analyze the history of a database schema.The method makes use of mining software repositories (MSR) techniques.We report on the application of the method to a large-scale case study.

Where was this SQL query executed? a static concept location approach

Concept location in software engineering is the process of identifying where a specific concept is implemented in the source code of a software system. It is a very common task performed by developers during development or maintenance, and many techniques have been studied by researchers to make it more efficient. However, most of the current techniques ignore the role of a database in the architecture of a system, which is also an important source of concepts or dependencies among them. In this paper, we present a concept location technique for data-intensive systems, as systems with at least one database server in their architecture which is intensively used by its clients. Specifically, we present a static technique for identifying the exact source code location from where a given SQL query was sent to the database. We evaluate our technique by collecting and locating SQL queries from testing scenarios of two open source Java systems under active development. With our technique, we are able to successfully identify the source of most of these queries.

Static Analysis of Dynamic Database Usage in Java Systems

Understanding the links between application programs and their database is useful in various contexts such as migrating information systems towards a new database platform, evolving the database schema, or assessing the overall system quality. In the case of Java systems, identifying which portion of the source code accesses which portion of the database may prove challenging. Indeed, Java programs typically access their database in a dynamic way. The queries they send to the database server are built at runtime, through String concatenations, or Object-Relational Mapping frameworks like Hibernate and JPA. This paper presents a static analysis approach to program-database links recovery, specifically designed for Java systems. The approach allows developers to automatically identify the source code locations accessing given database tables and columns. It focuses on the combined analysis of JDBC, Hibernate and JPA invocations. We report on the use of our approach to analyse three real-life Java systems.

DAHLIA: A visual analyzer of database schema evolution

In a continuously changing environment, software evolution becomes an unavoidable activity. The mining software repositories (MSR) field studies the valuable data available in software repositories such as source code version-control systems, issue/bug-tracking systems, or communication archives. In recent years, many researchers have used MSR techniques as a way to support software understanding and evolution. While many software systems are data-intensive, i.e., their central artefact is a database, little attention has been devoted to the analysis of this important system component in the context of software evolution. The goal of our work is to reduce this gap by considering the database evolution history as an additional information source to aid software evolution. We present DAHLIA (Database ScHema EvoLutIon Analysis), a visual analyzer of database schema evolution. Our tool mines the database schema evolution history from the software repository and allows its interactive, visual analysis. We describe DAHLIA and present our novel approach supporting data-intensive software evolution.

Detecting and Preventing Program Inconsistencies under Database Schema Evolution

Nowadays, data-intensive applications tend to access their underlying database in an increasingly dynamic way. The queries that they send to the database server are usually built at runtime, through String concatenation, or Object-Relational-Mapping (ORM) frameworks. This level of dynamicity significantly complicates the task of adapting application programs to database schema changes. Failing to correctly adapt programs to an evolving database schema results in program inconsistencies, which in turn may cause program failures. In this paper, we present a tool-supported approach, that allows developers to (1) analyze how the source code and database schema co-evolved in the past and (2) simulate a database schema change and automatically determine the set of source code locations that would be impacted by this change. Developers are then provided with recommendations about what they should modify at those source code locations in order to avoid inconsistencies. The approach has been designed to deal with Java systems that use dynamic data access frameworks such as JDBC, Hibernate and JPA. We motivate and evaluate the proposed approach, based on three real-life systems of different size and nature.

Understanding the database manipulation behavior of programs

Due to the lack of (up-do-date) documentation, software maintenance and evolution processes often necessitate the recovery of a sucient understanding of the software system, before the latter can be adapted to new or changing requirements. To address this problem, several program comprehension techniques have been proposed to support this preliminary phase of software maintenance and evolution. Nevertheless, those techniques generally fail in gaining a complete and accurate understanding in the case of modern data-intensive systems, which are characterized by complex, dynamic and continuous interactions between the application programs and their database. In particular, understanding the database manipulation behavior of a given program involves dierent levels of comprehension ranging from identifying to relating and interpreting the successive database access operations. In this paper, we present our early research achievements in the development of a tool-supported framework aiming to extract and understand the database manipulation behavior of data-intensive programs.

DAHLIA 2.0: A Visual Analyzer of Database Usage in Dynamic and Heterogeneous Systems

Understanding the links between application programs and their database is useful in various contexts such as migrating information systems towards a new database platform, evolving the database schema, or assessing the overall system quality. However, data-intensive applications nowadays tend to access their underlying database in an increasingly dynamic way. The queries that they send to the database server are usually built at runtime, through String concatenation, or Object-Relational-Mapping (ORM) frameworks. This level of dynamicity significantly complicates the task of adapting programs to an evolving database schema. In this paper, we present DAHLIA 2.0, an interactive visualization tool that allows developers to analyze the database usage in order to support data-intensive software evolution and more precisely, program-database co-evolution.

Supporting schema evolution in schema-less NoSQL data stores

NoSQL data stores are becoming popular due to their schema-less nature. They offer a high level of flexibility, since they do not require to declare a global schema. Thus, the data model is maintained within the application source code. However, due to this flexibility, developers have to struggle with a growing data structure entropy and to manage legacy data. Moreover, support to schema evolution is lacking, which may lead to runtime errors or irretrievable data loss, if not properly handled. This paper presents an approach to support the evolution of a schema-less NoSQL data store by analyzing the application source code and its history. We motivate this approach on a subject system and explain how useful it is to understand the present database structure and facilitate future developments.