Academic journal article Informatica Economica

An UI Layout Files Analyzer for Test Data Generation

Academic journal article Informatica Economica

An UI Layout Files Analyzer for Test Data Generation

Article excerpt

1 Introduction

S oftware testing is an important phase within the software development cycle. It is also an expensive phase counting about 40% of the total cost associated with software development [1], [2].

According to ISO9000 standard [13], the quality associated to a deliverable is seen as being "the degree to which a set of inherent characteristics fulfill requirements". If the requirements are not (completely) met, severe consequences may follow, like intensive rework or even the risk of losing the business.

From an economic point of view, quality measures are indicating the application actual metrics (in terms of cost, time and scope) that can be easily compared with the planned ones. To fulfill the requirements is crucial but the way in which these requirements are met in terms of time, cost and application scope is also very important because an application may be delivered in time but with a lot extra work and/or by using lighter inspections, so the project may not be so successful due to the reduced profits and very possible future implementation costs.

When discussing about the quality of a software application, usually there are two types of costs assigned [14]:

§ poor quality costs - defects that must be repaired by corrective actions;

§ good quality costs - code inspections and prevention activities, like planning, training and auditing.

As illustrated into the Figure 1, any application can reach a great quality level at a reasonable cost by positioning itself just above the optimal point indicated by the vertical line.

Testing methodologies include white-box and black-box approaches [3], [4]. White-box testing considers the internal structure of programs. In this case, test data generation uses several coverage criteria (path, statement, branch etc.). Black-box testing does not depend on the internal structure of programs. Test data is generated based on program specifications.

One area of software testing that can be automated is test data generation. Test data generation based on coverage can use random or optimized inputs, engaging advanced techniques [5], [6].

There is a continuous uptrend of mobile application development. Compared with other software solutions, time-to-market for mobile applications tends to be shorter. A mobile application can be used on different versions of the target platform, on different hardware configurations (screen size, processor architecture, processor speed, memory capacity etc.), different types of networks (Wi-Fi, mobile) etc. In this respect, mobile applications testing is more challenging than other type of application testing [7], [8], [9]. Every improvement of testing process for mobile applications helps achieving high quality software, reducing the development costs and the time-to-market. We propose a complex system for automated test data generation for mobile applications in order to reduce the required time for testing while keeping the same level of quality. This integrates our previous work that includes researches related to test data generators based on source code as in [6] and [10] where we proposed a test data generator using genetic algorithms and a framework for test data generators analysis. The system combines several inputs for test data generator in order to achieve an optimized set of inputs that assure the best coverage of code and the input domain.

The paper is organized as follows: Section 2 describes the proposed framework for test data generators for mobile applications and the Data Specification Language (DSL). Section 3 presents the proposed components of the test data generator that are based on source code, user interface layout files and specification. Section 4 presents the same example implemented for two mobile platforms (Android and Windows Phone) in order to highlight the UI layout files challenges. Section 5 is dedicated to the discussion related to presented work. The paper ends with conclusions and future work. …

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