Implementation of SMES Address Information Data Processing Database Design Using Python Programming

This study aims to implement a database using Flask framework in Python programming language. The database management system used was MySQL. The database contained data about SME addresses. There were 5 interrelated tables that were the table of UKM, the table of alamat_UKM, the table of kelurahan, the table of kecamatan, and the table of kabupaten. The researchers applied the waterfall model. This model was carried out by taking a sequential approach in software development. The steps in this model were system and software requirements, analysis, design, coding, testing, and maintenance. This study resulted in 2 applications that were the Python-based application which was a server-side program and the website-based application which was a client-side program. In developing the applications, all processes in the database management system consisting of CRUDs (Create, Read, Update, Delete and Search) were developed, implemented, and processed in accordance with the transactional process in database management

Virtual Mutation Analysis of Relational Database Schemas

Relational databases are a vital component of many modern soft- ware applications. Key to the definition of the database schema — which 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

Sanitizing and Minimizing Databases for Software Application Test Outsourcing

Abstract—Testing software applications that use nontrivial databases is increasingly outsourced to test centers in order to achieve lower cost and higher quality. Not only do different data privacy laws prevent organizations from sharing this data with test centers because databases contain sensitive information, but also this situation is aggravated by big data – it is time consum-ing and difficult to anonymize, distribute, and test with large databases. Deleting data randomly often leads to significantly worsened test coverages and fewer uncovered faults, thereby reducing the quality of software applications. We propose a novel approach for Protecting and mInimizing databases for Software TestIng taSks (PISTIS) that both sanitizes and minimizes a database that comes along with an application. PISTIS uses a weight-based data clustering algorithm that partitions data in the database using information obtained using program analysis that describes how this data is used by the application. For each cluster, a centroid object is computed that represents different persons or entities in the cluster, and we use associative rule mining to compute and use constraints to ensure that the centroid objects are representative of the general population of the data in the cluster. Doing so also sanitizes information, since these centroid objects replace the original data to make it difficult for attackers to infer sensitive information. Thus, we reduce a large database to a few centroid objects and we show in our experiments with two applications that test coverage stays within a close range to its original level. I

A Comprehensive Framework for Testing Database-Centric Software Applications

The database is a critical component of many modern software applications. Recent reports indicate that the vast majority of database use occurs from within an application program. Indeed, database-centric applications have been implemented to create digital libraries, scientific data repositories, and electronic commerce applications. However, a database-centric application is very different from a traditional software system because it interacts with a database that has a complex state and structure. This dissertation formulates a comprehensive framework to address the challenges that are associated with the efficient and effective testing of database-centric applications. The database-aware approach to testing includes: (i) a fault model, (ii) several unified representations of a program's database interactions, (iii) a family of test adequacycriteria, (iv) a test coverage monitoring component, and (v) tools for reducing and re-ordering a test suite during regression testing.This dissertation analyzes the worst-case time complexity of every important testing algorithm. This analysis is complemented by experiments that measure the efficiency and effectiveness of thedatabase-aware testing techniques. Each tool is evaluated by using it to test six database-centric applications. The experiments show thatthe database-aware representations can be constructed with moderate time and space overhead. The adequacy criteria call for test suitesto cover 20% more requirements than traditional criteria and this ensures the accurate assessment of test suite quality. It is possibleto enumerate data flow-based test requirements in less than one minute and coverage tree path requirements are normally identified in no morethan ten seconds. The experimental results also indicate that the coverage monitor can insert instrumentation probes into all six of theapplications in fewer than ten seconds. Although instrumentation may moderately increase the static space overhead of an application, the coverage monitoring techniques only increase testing time by 55% on average. A coverage tree often can be stored in less than five seconds even though the coverage report may consume up to twenty-fivemegabytes of storage. The regression tester usually reduces or prioritizes a test suite in under five seconds. The experiments also demonstrate that the modified test suite is frequently more streamlined than the initial tests

Pengujian Perangkat Lunak Sistem Informasi Penilaian Mahasiswa dengan Teknik Boundary Value Analysis Menggunakan Metode Black Box Testing

In this student assessment application, if there is an error program, there will be losses for students and universities. Losses for students, if this application error occurs, then the assessment received by students will experience delays due to the difficulty in the process of calculating student grades and students will be late to receive an assessment. Losses for the university, if there is an error program on this application, the university will suffer losses if students want a rapid decline in grades but the university cannot calculate quickly and accurately. The method used in testing this application uses the black box method with boundary value analysis techniques. Boundary value analysis techniques test the quality of the application by showing that there are still some errors when entering data to be tested in the column to determine whether the input value is valid or not with the lower limit and the upper limit that has been determined. So the problems that occur can cause data stored in the database does not match the expected data. The test is carried out in the Student Rating form by testing the upper and lower limit values through several predetermined stages for each column contained in the form. Test results can be used as input to improve the application. The results of this test can provide a reference for testing the quality of student assessment applications by analyzing the functionality of the program that has been made in accordance with the expected results

A Symbolic Execution Algorithm for Constraint-Based Testing of Database Programs

In so-called constraint-based testing, symbolic execution is a common technique used as a part of the process to generate test data for imperative programs. Databases are ubiquitous in software and testing of programs manipulating databases is thus essential to enhance the reliability of software. This work proposes and evaluates experimentally a symbolic ex- ecution algorithm for constraint-based testing of database programs. First, we describe SimpleDB, a formal language which offers a minimal and well-defined syntax and seman- tics, to model common interaction scenarios between pro- grams and databases. Secondly, we detail the proposed al- gorithm for symbolic execution of SimpleDB models. This algorithm considers a SimpleDB program as a sequence of operations over a set of relational variables, modeling both the database tables and the program variables. By inte- grating this relational model of the program with classical static symbolic execution, the algorithm can generate a set of path constraints for any finite path to test in the control- flow graph of the program. Solutions of these constraints are test inputs for the program, including an initial content for the database. When the program is executed with respect to these inputs, it is guaranteed to follow the path with re- spect to which the constraints were generated. Finally, the algorithm is evaluated experimentally using representative SimpleDB models.Comment: 12 pages - preliminary wor