A survey on software testability

Context: Software testability is the degree to which a software system or a unit under test supports its own testing. To predict and improve software testability, a large number of techniques and metrics have been proposed by both practitioners and researchers in the last several decades. Reviewing and getting an overview of the entire state-of-the-art and state-of-the-practice in this area is often challenging for a practitioner or a new researcher. Objective: Our objective is to summarize the body of knowledge in this area and to benefit the readers (both practitioners and researchers) in preparing, measuring and improving software testability. Method: To address the above need, the authors conducted a survey in the form of a systematic literature mapping (classification) to find out what we as a community know about this topic. After compiling an initial pool of 303 papers, and applying a set of inclusion/exclusion criteria, our final pool included 208 papers. Results: The area of software testability has been comprehensively studied by researchers and practitioners. Approaches for measurement of testability and improvement of testability are the most-frequently addressed in the papers. The two most often mentioned factors affecting testability are observability and controllability. Common ways to improve testability are testability transformation, improving observability, adding assertions, and improving controllability. Conclusion: This paper serves for both researchers and practitioners as an "index" to the vast body of knowledge in the area of testability. The results could help practitioners measure and improve software testability in their projects

Software quality and reliability prediction using Dempster -Shafer theory

As software systems are increasingly deployed in mission critical applications, accurate quality and reliability predictions are becoming a necessity. Most accurate prediction models require extensive testing effort, implying increased cost and slowing down the development life cycle. We developed two novel statistical models based on Dempster-Shafer theory, which provide accurate predictions from relatively small data sets of direct and indirect software reliability and quality predictors. The models are flexible enough to incorporate information generated throughout the development life-cycle to improve the prediction accuracy.;Our first contribution is an original algorithm for building Dempster-Shafer Belief Networks using prediction logic. This model has been applied to software quality prediction. We demonstrated that the prediction accuracy of Dempster-Shafer Belief Networks is higher than that achieved by logistic regression, discriminant analysis, random forests, as well as the algorithms in two machine learning software packages, See5 and WEKA. The difference in the performance of the Dempster-Shafer Belief Networks over the other methods is statistically significant.;Our second contribution is also based on a practical extension of Dempster-Shafer theory. The major limitation of the Dempsters rule and other known rules of evidence combination is the inability to handle information coming from correlated sources. Motivated by inherently high correlations between early life-cycle predictors of software reliability, we extended Murphy\u27s rule of combination to account for these correlations. When used as a part of the methodology that fuses various software reliability prediction systems, this rule provided more accurate predictions than previously reported methods. In addition, we proposed an algorithm, which defines the upper and lower bounds of the belief function of the combination results. To demonstrate its generality, we successfully applied it in the design of the Online Safety Monitor, which fuses multiple correlated time varying estimations of convergence of neural network learning in an intelligent flight control system

Risk Assessment of LNG Storages using LOPA and FTA: An Integrated Approach

PresentationLiquefied Natural Gas (LNG), an economically attractive and environmental friendly fuel is the current energy alternative across the globe. Its market potential and high demand is felt currently in the Indian subcontinent as well. Government and private players are seriously getting into this energy option and establishing many LNG facilities on the west and east coast of India. While establishing in this new energy sector it is vital to identify and analyse the safety hazards likely to affect public and environment. LNG being a flammable chemical, loss of its containment manifests to consequences in terms of fire, explosion and other impacts. There are several methods currently available to carry out the risk analysis of such projects. LOPA is a quick and simple technique applied to determine the risk by estimate consequence frequencies. But application of LOPA becomes constrained when failures are compound and safety systems are integrated. Fault Tree Analysis (FTA) was integrated into LOPA to eliminate this draw back. FTA was used to find out the probability of failure on demand (PFD) of integrated protection layers. This FTA-LOPA integrated approach was used as an effective tool in this work to study hazard potentials and estimate the consequences due to such hazards. Based on the technical specifications provided and description of the work, the LOC scenarios are identified in the facility from the HAZOP study

Supervisory Control System Architecture for Advanced Small Modular Reactors

This technical report was generated as a product of the Supervisory Control for Multi-Modular SMR Plants project within the Instrumentation, Control and Human-Machine Interface technology area under the Advanced Small Modular Reactor (SMR) Research and Development Program of the U.S. Department of Energy. The report documents the definition of strategies, functional elements, and the structural architecture of a supervisory control system for multi-modular advanced SMR (AdvSMR) plants. This research activity advances the state-of-the art by incorporating decision making into the supervisory control system architectural layers through the introduction of a tiered-plant system approach. The report provides a brief history of hierarchical functional architectures and the current state-of-the-art, describes a reference AdvSMR to show the dependencies between systems, presents a hierarchical structure for supervisory control, indicates the importance of understanding trip setpoints, applies a new theoretic approach for comparing architectures, identifies cyber security controls that should be addressed early in system design, and describes ongoing work to develop system requirements and hardware/software configurations

Integration of software reliability into systems reliability optimization

Reliability optimization originally developed for hardware systems is extended to incorporate software into an integrated system reliability optimization. This hardware-software reliability optimization problem is formulated into a mixed-integer programming problem. The integer variables are the number of redundancies, while the real variables are the components reliabilities;To search a common framework under which hardware systems and software systems can be combined, a review and classification of existing software reliability models is conducted. A software redundancy model with common-cause failure is developed to represent the objective function. This model includes hardware redundancy with independent failure as a special case. A software reliability-cost function is then derived based on a binomial-type software reliability model to represent the constraint function;Two techniques, the combination of heuristic redundancy method with sequential search method, and the Lagrange multiplier method with the branch-and-bound method, are proposed to solve this mixed-integer reliability optimization problem. The relative merits of four major heuristic redundancy methods and two sequential search methods are investigated through a simulation study. The results indicate that the sequential search method is a dominating factor of the combination method. Comparison of the two proposed mixed-integer programming techniques is also studied by solving two numerical problems, a series system with linear constraints and a bridge system with nonlinear constraints. The Lagrange multiplier method with the branch-and-bound method has been shown to be superior to all other existing methods in obtaining the optimal solution;Finally an illustration is performed for integrating software reliability model into systems reliability optimization

Categorizing Non-Functional Requirements Using a Hierarchy in UML.

Non-functional requirements (NFRs) are a subset of requirements, the means by which software system developers and clients communicate about the functionality of the system to be built. This paper has three main parts: first, an overview of how non-functional requirements relate to software engineering is given, along with a survey of NFRs in the software engineering literature. Second, a collection of 161 NFRs is diagrammed using the Unified Modelling Language, forming a tool with which developers may more easily identify and write additional NFRs. Third, a lesson plan is presented, a learning module intended for an undergraduate software engineering curriculum. The results of presenting this learning module to a class in Spring, 2003 is presented

Aplicaciones de la teoría de la información y la inteligencia artificial al testing de software

Tesis inédita de la Universidad Complutense de Madrid, Facultad de Informática, Departamento de Ingeniería de Sistemas lnformáticos y de Computación, leída el 4-05-2022Software Testing is a critical field for the software industry, as it has the main tools used to ensure the reliability of the produced software. Currently, mor then 50% of the time and resources for creating a software product are diverted to testing tasks, from unit testing to system testing. Moreover, there is a huge interest into automatising this field, as software gets bigger and the amount of required testing increases. however, software Testing is not only an industry oriented field; it is also a really interesting field with a noble goal (improving the reliability of software systems) that at the same tieme is full of problems to solve....Es Testing Software es un campo crítico para la industria del software, ya que éste contienen las principales herramientas que se usan para asegurar la fiabilidad del software producido. Hoy en día, más del 50% del tiempo y recursos necesarios para crear un producto software son dirigidos a tareas de testing, desde el testing unitario al testing a nivel de sistema. Más aún, hay un gran interés en automatizar este campo, ya que el software cada vez es más grande y la cantidad de testing requerido crece. Sin embargo, el Testing de Software no es solo un campo orientado a la industria; también es un campo muy interesante con un objetivo noble (mejorar la fiabilidad de los sistemas software) que al mismo tiempo está lleno de problemas por resolver...Fac. de InformáticaTRUEunpu

Understanding the role of sensor optimisation in complex systems

Complex systems involve monitoring, assessing, and predicting the health of various systems within an integrated vehicle health management (IVHM) system or a larger system. Health management applications rely on sensors that generate useful information about the health condition of the assets; thus, optimising the sensor network quality while considering specific constraints is the first step in assessing the condition of assets. The optimisation problem in sensor networks involves considering trade-offs between different performance metrics. This review paper provides a comprehensive guideline for practitioners in the field of sensor optimisation for complex systems. It introduces versatile multi-perspective cost functions for different aspects of sensor optimisation, including selection, placement, data processing and operation. A taxonomy and concept map of the field are defined as valuable navigation tools in this vast field. Optimisation techniques and quantification approaches of the cost functions are discussed, emphasising their adaptability to tailor to specific application requirements. As a pioneering contribution, all the relevant literature is gathered and classified here to further improve the understanding of optimal sensor networks from an information-gain perspective