Abstract — Mutation Testing is a fault–based software testing technique that has been widely studied for over three decades. The literature on Mutation Testing has contributed a set of approaches, tools, developments and empirical results which have not been surveyed in detail until now. This paper provides a comprehensive analysis and survey of Mutation Testing. The paper also presents the results of several development trend analyses. These analyses provide evidence that Mutation Testing techniques and tools are reaching a state of maturity and applicability, while the topic of Mutation Testing itself is the subject of increasing interest. Index Terms — mutation testing, survey I.

Abstract. Several researchers are using evolutionary search methods to search for test data with which to test a program. The fitness or cost function depends on the test goal but almost invariably an important component of the cost function is an estimate of the cost of satisfying a predicate expression as might occur in branches, exception conditions, etc. This paper reviews the commonly used cost functions and points out some deficiencies. Alternative cost functions are proposed to overcome these deficiencies. The evidence from an experiment is that they are more reliable. 1

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PERSONAL INFORMATION • Nationality: Jordanian.

This report documents the program and the outcomes of Dagstuhl Seminar 13021 “Symbolic Methods in Testing”. The aim of the seminar was to bring together leading researchers of this field; the seminary ended up with 38 participants from 10 countries: France, The Netherlands,

Abstract---Computation of complex networks has become a greatest zeal in the present day scenario as a result of which the artificial network has been established. A Neural Network is a trained network analysis with a large number of interconnected groups of networks called the artificial neurons that process information in a similar way as the human brain. An ANN changes its structure based on the processing data input i.e. the information that flows through the system during the processing period and it is called an adaptive system. The system computes the information with the help of the algorithm that computes the data through the various segment of the signal processing flowing in a sequential order. This technique has a wide application in the field of medicine where it is used to detect the lung cancer, breast cancer, prostate cancer etc., through the network analysis. They are also used in the robotic arm, computation of a sequential conventional system etc.

The series of UK Software Testing Research workshops started with a workshop at the University of York in September 1999. This first event involved only invited speakers and participants and almost all were from the UK. The EPSRC funded FORTEST network was established in 2001 and discussions during FORTEST workshops led to a second event being held in September 2003. This second workshop was open to the international testing community and all papers were reviewed by members of the programme committee. UKTest 2005 has followed this pattern. The aim of UKTest is to bring together members of the UK Software Testing research community. It is intended to be an informal event and so presentations are relatively short (most are 20 minutes) and plenty of time has been scheduled for questions and discussion.

I would like to gratefully acknowledge the kind assistance of my supervisor Dr. Les Johnson. In addition I gained much from discussions with Professor Edwin Borg-Costanzi and fellow students,

I would like to express my sincere gratitude to my supervisors, Ina Schieferdecker and Joachim Wegener, for their professional guidance, inspiring discussions, and encouragement during the period of this research. My thanks are also due to Stefan Jähnichen for his broad support and guidance. Furthermore, I would like to thank all my collegues from both Daimler AG and the Technical University of Berlin. In particular, I thank all my DCAITI team members for the very good cooperation and time we had together:

Finite state transition models such as State Machines (SMs) have become a prevalent paradigm for the description of dynamic systems. Such models are well-suited to modelling the behaviour of complex systems, including in conditions of failure, and where the order in which failures and fault events occur can affect the overall outcome (e.g. total failure of the system). For the safety assessment though, the SM failure behavioural models need to be converted to analysis models like Generalised Stochastic Petri Nets (GSPNs), Markov Chains (MCs) or Fault Trees (FTs). This is particularly important if the transformed models are supported by safety analysis tools. This thesis, firstly, identifies a number of problems encountered in current safety analysis techniques based on SMs. One of the existing approaches consists of transforming the SMs to analysis-supported state-transition formalisms like GSPNs or MCs, which are very powerful in capturing the dynamic aspects and in the evaluation of safety measures. But in this approach, qualitative analysis is not encouraged; here the focus is primarily on probabilistic analysis. Qualitative analysis is particularly important when probabilistic data are not available (e.g., at early stages of design). In an alternative approach though, the generation of combinatorial, Boolean FTs has been applied to SM-based models. FTs are well-suited to qualitative analysis, but cannot capture the significance of the temporal order of events expressed by SMs. This makes the approach potentially error prone for the analysis of dynamic systems. In response, we propose a new SM-based safety analysis technique which converts SMs to Temporal Fault Trees (TFTs) using Pandora — a recent technique for introducing temporal logic to FTs. Pandora provides a set of temporal laws, which allow the significance of the SM temporal semantics to be preserved along the logical analysis, and thereby enabling a true qualitative analysis of a dynamic system. The thesis develops algorithms for conversion of SMs to TFTs. It also deals with the issue of scalability of the approach by proposing a form of compositional synthesis in which system large TFTs can be generated from individual component SMs using a process of composition. This has the dual benefits of allowing more accurate analysis of different sequences of faults, and also helping to reduce the cost of performing temporal analysis by producing smaller, more manageable TFTs via the compositionality. The thesis concludes that this approach can potentially address limitations of earlier work and thus help to improve the safety analysis of increasingly complex dynamic safety-critical systems.