Context Database evolution involves processes such as the evolution of the schema, the adaptation of the application to the new schema, and migrations of data to the new or modified structures of the schema. Data migration is particularly crucial in databases where data repetition is common such as the NoSQL column family DBMSs. In these systems, data integrity cannot be enforced from the database side, but instead needs to be maintained from the application side. Database evolution is also affected by data repetition and the absence of data integrity enforcement from the database, as any evolution of the schema requires data migrations to maintain data integrity. Objectives Ensure data integrity in NoSQL column family DBMSs during database evolution by providing specific instructions for the execution of the necessary data migrations. Methods We propose MoDEvo, a model-driven engineering approach that provides a data migration model to ensure data integrity for database evolution in column-family DBMSs. This model is then transformed into an executable script that implements the migration procedures. Results We evaluate MoDEvo by executing data migrations in case studies obtained from open-source projects where the schema evolved. In this evaluation we use Apache Cassandra, the most popular column-family DBMS. Through this evaluation, we verify that the scripts generated from the data migration model effectively maintain data integrity within the database. Conclusion MoDEvo aids database evolution in column family DBMSs by avoiding the incurrence in the creation of inconsistencies and can also detect impossible migrations, thereby preventing errors. There is still room for improvement such as extending the supported databases to other paradigms where data repetition is common and addressing the evolution of the client applications alongside schema evolution.

Software testing is an essential knowledge area required by industry for software engineers. However, software engineering students often consider testing less appealing than designing or coding. Consequently, it is difficult to engage students to create effective tests. To encourage students, we explored the use of gamification and investigated whether this technique can help to improve the engagement and performance of software testing students. We conducted a controlled experiment to compare the engagement and performance of two groups of students that took an undergraduate software testing course in different academic years. The experimental group is formed by 135 students from the gamified course whereas the control group is formed by 100 students from the non-gamified course. The data collected were statistically analyzed to answer the research questions of this study. The results show that the students that participated in the gamification experience were more engaged and achieved a better performance. As an additional finding, the analysis of the results reveals that a key aspect to succeed is the gamification experience design. It is important to distribute the motivating stimulus provided by the gamification throughout the whole experience to engage students until the end. Given these results, we plan to readjust the gamification experience design to increase student engagement in the last stage of the experience, as well as to conduct a longitudinal study to evaluate the effects of gamification.

In recent years, software applications have been working with NoSQL databases as they have emerged to handle big data more efficiently than traditional databases. The data models of these databases are designed to satisfy the requirements of the software application, which means that the models must evolve when the requirements of the software application change. To avoid mistakes during the design and evolution of these NoSQL models, there are several methodologies that recommend using a conceptual model. This implies that consistency between the conceptual model and the schema must be maintained when either evolving the database or the software application. In this work, we propose CoDEvo, a model-driven engineering approach that uses model transformations to address the evolution of a NoSQL column family DBMS schema when the underlying conceptual model evolves due to software requirement changes, aiming to maintain consistency between the schema and conceptual model. We have addressed this problem by defining transformation rules that determine how to evolve the schema for a specific conceptual model change. To validate these transformations, we applied them to conceptual model changes from 9 open-source software applications, comparing the output schemas from CoDEvo with the schemas that were defined in these applications.

Database schemas evolve over time to satisfy changing application requirements. If this evolution is not performed correctly, some quality attributes are at risk such as data integrity, functional correctness, or maintainability. To help developer teams in the design of database schemas, several design methodologies for NoSQL databases have proposed to use conceptual models during this process. The use of an explicit conceptual model can also help developers in the tasks of schema evolution. In this work-in-progress paper, we propose a framework that, given a change in the conceptual model, identifies what must be modified in a NoSQL database schema and the underlying data. We researched several open source projects that use Apache Cassandra to study the benefits of using a conceptual model during the schema evolution process as well as to understand how these models evolve. In this first work, we have focused on studying seven types of conceptual model changes identified in these projects. For each change we describe the transformation required in the database schema to maintain the consistency between the schema and the model as well as the migration of data required to the new schema version.

The use of NoSQL databases for cloud environments has been increasing due to their performance advantages when working with big data.One of the most popular NoSQL databases used for cloud services is Cassandra, in which each table is created to satisfy one query.This means that as the same data could be retrieved by several queries, these data may be repeated in several different tables.The integrity of these data must be maintained in the application that works with the database, instead of in the database itself as in relational databases.In this paper, we propose a method to ensure the data integrity when there is a modification of data by using a conceptual model that is directly connected to the logical model that represents the Cassandra tables.This method identifies which tables are affected by the modification of the data and also proposes how the data integrity of the database may be ensured.We detail the process of this method along with two examples where we apply it in two insertions of tuples in a conceptual model.We also apply this method to a case study where we insert

The use of NoSQL databases has recently been increasing, being Cassandra one of the most popular ones.In Cassandra, each table is created to satisfy one query, so, as the same information could be retrieved by several queries, this information may be found in several distinct tables.However, the lack of mechanisms to ensure the integrity of the data means that the integrity could be broken after a modification of data.In this paper, we propose a method for keeping the integrity of the data by using a conceptual model that is directly connected to the logical model that represents the Cassandra tables.Our proposal aims to keep the data integrity automatically by providing a process that will undertake such maintenance when there is a modification of the data in the database.The conceptual model will be used to identify the tables that could have inconsistencies and also assist in resolving them.We also apply this approach to a case study where, given several insertions of tuples in the conceptual model, we determine what is needed to keep the logical integrity.

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.

Abstract In the field of database applications a considerable part of the business logic is implemented using a semi‐declarative language: the Structured Query Language (SQL). Because of the different semantics of SQL compared with other procedural languages, the conventional coverage criteria for testing are not directly applicable. This paper presents a criterion specifically tailored for SQL queries (SQLFpc). It is based on Masking Modified Condition Decision Coverage (MCDC) or Full Predicate Coverage and takes into account a wide range of the syntax and semantics of SQL, including selection, joining, grouping, aggregations, subqueries, case expressions and null values. The criterion assesses the coverage of the test data in relation to the query that is executed and it is expressed as a set of rules that are automatically generated and efficiently evaluated against a test database. The use of the criterion is illustrated in a case study, which includes complex queries. Copyright © 2010 John Wiley & Sons, Ltd.

Many software applications have a component based on database management systems in which information is generally handled through SQL queries embedded in the application code. When automation of software testing is mentioned in the research, this is normally associated with programs written in imperative and structured languages. However, the problem of automated software testing applied to programs that manage databases using SQL is still an open issue. This paper presents a measurement of the coverage of SQL queries and the tool that automates it. We also show how database test data may be revised and changed using this measurement by means of completing or deleting information to achieve the highest possible value of coverage of queries that have access to the database.