Environment for Test and Analysis of Distributed Software (ETADS)

With the advent of the mobile Internet era, the quality of Android application software and the level of user experience have become the key factors that determine the success or failure of market competition. The construction of software development and testing environment is an important part of the whole software development process. It is imperative to use machines instead of manpower to complete complicated tests that require precision. The concept of automated testing arises at the historic moment. The construction of software development and testing environment is an important link in the whole software development process. Different versions of operating systems, databases, network servers and application services, combined with different system architectures, make the types of software testing environments to be constructed various. The opening of Android system makes the development of Android Software easier. Any developer, whether a professional company or an individual, can develop their own applications. The combination of automated testing and manual testing makes up for the shortcomings found in automated testing, which requires a lot of initial investment and special personnel to maintain.KeywordsSoftware developmentAndroidAutomation

Smart contracts on blockchain platforms are susceptible to security issues that can lead to significant financial losses. This study converts the Solidity code into abstract syntax trees and generates control flow graphs and data flow graphs. These graphs train a graph convolutional network model to detect security weaknesses. The proposed system outperforms traditional tools, achieving higher accuracy, recall, precision, and F1 scores when detecting weaknesses such as integer overflow/underflow, reentrancy, delegate call to the untrusted callee, and time-based issues. This study demonstrates that leveraging control and data flow analysis with graph neural networks significantly enhances smart contract security and provides a robust and reliable solution.

Software test environments (STEs) provide a means of automating the test process and integrating testing tools to support required testing capabilities across the test process. Specifically, STEs may support test planning, test management, test measurement, test failure analysis, test development and test execution. The software architecture of an STE describes the allocation of the environment's functions to specific implementation structures. An STE's architecture can facilitate or impede modifications such as changes to processing algorithms, data representation or functionality. Performance and reusability are also subject to architecturally imposed constraints. Evaluation of an STE's architecture can provide insight into modifiability, extensibility, portability and reusability of the STE. This paper proposes a reference architecture for STEs. Its analytical value is demonstrated by using SAAM (Software Architectural Analysis Method) to compare three software test environments: PROTest II (PROLOG Test Environment, Version II), TAOS (Testing with Analysis and Oracle Support), and CITE (CONVEX Integrated Test Environment).

Software testing is an important part of information systems (IS) development process. To achieve effectiveness in software testing, the participating IS professionals must apply software testing techniques. A review of the current IS curriculum models reveals that specific pedagogical guidelines are not available for instructing software testing techniques. This paper discusses the importance of software testing to IS development and maintenance, reviews the existing software testing techniques, and provides a pedagogical guideline for instructing software testing techniques in IS curricula.

Code optimization improves program performance through program analysis and program transformation, which transforms the program in an equivalent form. The basis of optimization is data flow analysis and control flow analysis. The paper first analyzes the characterization of Mgrid and the kernel Resid routine, including architecture analysis, data flow analysis, and dependence analysis, which is the basis of static optimization. Based on these characterization data, we first apply static optimization to Resid, including DMA access, data reuse, overlapping of computing and DMA, loop unrolling, common sub-expression extraction, Input Value Embedding, etc. and then loop blocking is applied to Resid, and genetic algorithm is utilized to search for the near-optimal blocking size, which further improves the performance of static optimization. The performance of Mgrid is improved by combing static analysis and empirical search, which is verified during the experiments.

Various information systems are widely used in information society era, and the demand for highly dependable system is increasing year after year. However, software testing for such a system becomes more difficult due to the enlargement and the complexity of the system. In particular, it is too difficult to test parallel and distributed systems sufficiently although dependable systems such as high-availability servers usually form parallel and distributed systems. To solve these problems, to propose a software testing environment for dependable parallel and distributed system using the cloud computing technology, named D-Cloud. D-Cloud includes Eucalyptus as the cloud management software, and FaultVM based on QEMU as the virtualization software, and D-Cloud frontend for interpreting test scenario. D-Cloud enables not only to automate the system configuration and the test procedure but also to perform a number of test cases simultaneously, and to emulate hardware faults flexibly. In this paper, present the concept and design of D-Cloud, and describe how to specify the system configuration and the test scenario. Furthermore, the preliminary test example as the software testing using D-Cloud was presented. Its result shows that D-Cloud allows to set up the environment easily, and to test the software testing for the distributed system.

Object-oriented technology has been widely used in the area of computer software. However, traditional software testing techniques cannot perform effectively on object-oriented software. The differences between object-oriented software testing and traditional software testing are compared and elaborated from three aspects: testing model, testing software and testing cases.

Software testing provides a means to reduce errors, cut maintenance and overall software costs. Numerous software development and testing methodologies, tools, and techniques have emerged over the last few decades promising to enhance software quality. While it can be argued that there has been some improvement it is apparent that many of the techniques and tools are isolated to a specific lifecycle phase or functional area. This paper presents a set of best practice models and techniques integrated in optimized and quantitatively managed software testing process (OptimalSQM), expanding testing throughout the SDLC. Further, we explained how can Quantitative Defect Management Model be enhanced to be practically useful for determining which activities need to be addressed to improve the degree of early and cost-effective software fault detection with assured confidence is proposed. To enable software designers to achieve a higher quality for their design, a better insight into quality predictions for their design choices, test plans improvement using Simulated Defect Removal Cost Savings model is offered in this paper.

In this paper, we propose a software vulnerability checker which takes rules describing vulnerability patterns and a source program as input and detects locations and paths of the patterns in the program. Simple and flow patterns for vulnerabilities are described as rules in the specification language we designed. The lightweight control and data flow analysis is necessary to detect flow patterns. Newly discovered vulnerability patterns can easily be added to the existing rules. We implement the detector in three parts: a pattern matcher which finds locations of vulnerabilities in source program, a flow graph constructor which extracts the control flow and data flow from the program, and a flow analyzer which finds program's vulnerable execution paths.

Embedded software testing is a hot topic in software testing field. It is a challenge to enhance the generalization, portability, and maintainability of embedded software testing environment (ESTE). In this paper, by applying virtual machine technology into embedded software simulation testing, a custom-build embedded software simulation testing virtual machine (ESSTVM) which is composed of data type, memory management, task management, communication, testing instruction set and testing files is presented firstly. And then, ESSTVM was applied in design of avionics software testing environment (ASTE). Based on the description of architecture of ASTE, the design and implementation of the testing development system and testing execution system are put forward. Practical application of ASTE in engineering shows that the method can improve the generalization, portability, and maintainability of ESSTE effectively. Next research steps are put forward to improve the real-time capacity and efficiency of testing execution system and extend it to be distributed.

Abstract: Software testing is the process of identifying bugs or errors in developed software. Consequently, this process offers interested individuals with precise details on the product's quality. Software testing is done not only to ensure that the program behaves correctly or not, but to check whether it satisfies the user’s requirements and expectations as well. The testing of banking software is very important since they handle millions of transactions and are related to customers directly. Proper testing of the banking applications ensures that all operations are carried out accurately and remain safe and secure. The main objective of this paper is to provide an overview of various software testing techniques, their implementation and significance on the banking applications of Bangladesh.

The following topics are dealt with: computer aided instruction; cloud computing; Internet; software engineering; feature extraction; mobile computing; genetic algorithms; business data processing; educational institutions; learning (artificial intelligence).

This article is an introduction to the FRIDGE design environment which supports the design and DSP implementation of fixed-point digital signal processing systems. We present the tool-supported transformation of signal processing algorithms coded in floating-point ANSI C to a fixed-point representation in SystemC. We introduce the novel approach to control and data flow analysis, which is necessary for the transformation. The design environment enables fast bit-true simulation by mapping the fixed-point algorithm to integral data types of the host machine. A speedup by a factor of 20 to 400 can be achieved compared to C++-library-based bit-true simulation. FRIDGE also provides a direct link to DSP implementation by processor specific C code generation and advanced code optimization.

Data Flow Analysis as expressed by Monotone Frameworks is often associated with classical imperative programming languages and has played a crucial role in the efficient implementation of these languages. Robin Milner's Calculus of Communicating Systems, CCS, is concerned with modelling concurrent systems and has mainly been analysed using types and control flow analyses. In the present paper we present an instance of a Monotone Framework together with a novel worklist algorithm for more precisely approximating the flow-sensitive control structure of even infinitary processes expressed in CCS.

In this paper, we propose a neural network-based model for optimal software testing and maintenance policy, where the software testing environment and the operational environment are characterized by an environmental factor. We also present a systematic study for defect detection and correction processes. In our proposed approach, we consider the logistic growth curve model and the constant correction time for defect prediction. Then, we estimate the jointly optimal software testing period and maintenance limit via minimization of a software cost function that takes into account the environmental factor and the imperfect fault removal. More precisely, the total expected cost is formulated via a discrete-type software reliability model based on the difference between operational environments, imperfect defect removal, and defect correction process. Experimental results on a real software data set are presented to demonstrate the effectiveness of the proposed approach in defect prediction as well as in determining the jointly optimal testing period and planned maintenance limit.