This dissertation critiques and rejects Elliott Sober's dismissal of intelligent design as a scientific hypothesis. Sober builds the case for this dismissal in chapter 2 of his 2008 book Evidence and Evolution. Sober's case against intelligent design as science is a philosophical one, emerging from a Bayesian likelihood approach. Sober claims that unlike neo-Darwinian processes, intelligent design cannot supply independent evidence to support the claim that it is a measurably likely cause responsible for the emergence of biological organisms and the structures or processes of which they are composed. Without an assessable likelihood, Sober asserts that intelligent design (again, unlike neo-Darwinian mechanisms) is not testable, and since it is not testable, it does not qualify as a scientific hypothesis. This dissertation argues however, that according to Sober's own standards in Evidence, because intelligent design and the neo-Darwinian hypothesis both address unrepeated, major biological changes in the unobservable past, and because they both depend upon crucial analogies in order to support either inductive arguments or likelihood assessments, the two hypotheses stand on equivalent evidential and logical grounds. Either Sober must reject both neo-Darwinism and intelligent design, or he must allow them both as equivalent, rival hypotheses based upon a fair application of his argumentation requirements. In addition, after explaining important basics of analogy theory, and its crucial, even unavoidable role in the historical (or "origins") sciences, the dissertation goes on to show how intelligent design's empirical support, based upon analogy with humanly designed artifacts, machines and increasingly cell-like creations in the laboratory, is continuing to grow stronger by the year in both likelihood and in explanatory power. The dissertation thus concludes that intelligent design should be treated as a viable scientific explanation for the dramatic examples of specified complexity being discovered in biology, and indeed should be regarded as an increasingly vigorous rival to the neo-Darwinian explanation of such complexity.

Debugging is an important and critical phase during the software development process. Software debugging is serious and tough practice involved in functional base test driven development. Software vendors encourages their programmers to practice test driven development during the initial development phases to capture the bug traces and the associated code coverage infected from diagnosed bugs. Application’s source code with fewer threats of bug existence or faulty executions is assumed as highly efficient and stable especially when real time software products are in consideration. Due to the fact that process of development of software projects relies on great number of users and testers which required having an effective fault localization technique. This specific fault localization technique can highlight the most critical areas of software system at code as well as modular level so that debugging algorithm can be used to debug the application source code. Nowadays many complex or simple software systems are in corporation with open bug repositories to localize the bugs. Any inconsistency or imperfection in early development phase of software product results in low efficient system and less reliability. Statistical debugging of program source code for visualization of fault is an important and efficient way to select and rank the suspicious lines of code. This research provides guidelines for practicing statistical debugging technique for programs coded in Ruby programming language. This thesis presents statistical debugging techniques available for dynamic programming languages. Firstly, the statistical debugging techniques were thoroughly observed with different predicate base approaches followed in previous work done in the subject area. Secondly, the new process of statistical debugging for programs coded in Ruby programming language is introduced by generating dynamic predicates. Results were analyzed by implementing multiple programs written in Ruby programming language with different complexity level. The analysis of experimentation performed on candidate programs depict that SOBER is more efficient and accurate in bug identification than Cause Isolation Scheme. It is concluded that despite of extensive research in the field of statistical debugging and fault localization it is not possible to identify majority of the bugs. Moreover SOBER and Cause Isolation Scheme algorithms are found to be two most mature and effective statistical debugging algorithms for bug identification with in software source code. / Address: School of Computing Blekinge Institute of Technology SE-371 79 Karlskrona, Sweden Phone: +46-(0)455-385804 Fax: +46-(0)455-385057

An intoxicated police employee can never keep the community safe and secure, as mandated by law enforcement prescripts. However, limited attention is given to harmful or hazardous substance abuse or the binge drinking habits of police employees. Substance abuse being a ‘culture’ in law enforcement agencies and the maintenance of the blue wall of silence as a protective measure necessitates scientific research to explore how a collaborative working relationship between the occupational social worker and especially First Line Managers (FLMs) can contribute to addressing this phenomenon in a timeous manner. The researcher applied a quantitative research approach and utilised a switching replication quasi-experimental design to determine whether the collaborative working relationship between South African Police Service (SAPS) FLMs and Police Social Workers (PSWs) can be strengthened to the extent that they effectively and efficiently deal with the harmful or hazardous substance abuse or binge drinking habits of SAPS employees by exposing the FLMs to a social work intervention, namely the Sober Workplace Programme for Managers. The pre-, mid-, and posttest measurements are based on knowledge, attitude, and behaviour constructs to determine if the two hypotheses formulated were supported. The study, however, did not indicate that the Sober Workplace Programme for Managers strengthens the collaborative working relationship between the FLMs and PSWs to address the harmful or hazardous substance abuse or binge drinking habits of employees in the workplace. Alternative research and occupational social work strategies are recommended to establish if and how the Sober Workplace Programme for Managers can be implemented to strengthen the collaborative working relationship between the FLMs and PSWs to address the harmful or hazardous substance abuse or binge drinking habits of employees. / Social Work / Ph. D. (Social Work)