Root Cause Analysis for software systems is a challenging diagnostic task due to complexity emanating from the interactions between system components. Furthermore, the sheer size of the logged data makes it often difficult for human operators and administrators to perform problem diagnosis and root cause analysis. The diagnostic task is further complicated by the lack of models that could be used to support the diagnostic process. Traditionally, this diagnostic task is conducted by human experts who create mental models of systems, in order to generate hypotheses and conduct the analysis even in the presence of incomplete logged data. A challenge in this area is to provide the necessary concepts, tools, and techniques for the operators to focus their attention to specific parts of the logged data and ultimately to automate the diagnostic process. The work described in this thesis aims at proposing a framework that includes techniques, formalisms, and algorithms aimed at automating the process of root cause analysis. In particular, this work uses annotated requirement goal models to represent the monitored systems' requirements and runtime behavior. The goal models are used in combination with log data to generate a ranked set of diagnostics that represent the combination of tasks that failed leading to the observed failure. In addition, the framework uses a combination of word-based and topic-based information retrieval techniques to reduce the size of log data by filtering out a subset of log data to facilitate the diagnostic process. The process of log data filtering and reduction is based on goal model annotations and generates a sequence of logical literals that represent the possible systems' observations. A second level of investigation consists of looking for evidence for any malicious (i.e., intentionally caused by a third party) activity leading to task failures. This analysis uses annotated anti-goal models that denote possible actions that can be taken by an external user to threaten a given system task. The framework uses a novel probabilistic approach based on Markov Logic Networks. Our experiments show that our approach improves over existing proposals by handling uncertainty in observations, using natively generated log data, and by providing ranked diagnoses. The proposed framework has been evaluated using a test environment based on commercial off-the-shelf software components, publicly available Java Based ATM machine, and the large publicly available dataset (DARPA 2000).

This thesis investigates issues like maintenance problems, key factors, maintenance challenges, maintenance solutions and practical difficulties in wind energy. In this case, surveys and interviews have been taken from several companies and maintenance experts, to find most prevailing problems and problem-solving methods since last few years. It helps to show, how the energy maintenance has been developed in past few years. Also it analyses the impact of fourth generation maintenance in wind energy production. From research questions, key factors involved in wind energy maintenance provides us with valuable suggestions to develop the maintenance methods in future vision.

Root Cause Analysis for software systems is a challenging diagnostic task due to complexity emanating from the interactions between system components. Furthermore, the sheer size of the logged data makes it often difficult for human operators and administrators to perform problem diagnosis and root cause analysis. The diagnostic task is further complicated by the lack of models that could be used to support the diagnostic process. Traditionally, this diagnostic task is conducted by human experts who create mental models of systems, in order to generate hypotheses and conduct the analysis even in the presence of incomplete logged data. A challenge in this area is to provide the necessary concepts, tools, and techniques for the operators to focus their attention to specific parts of the logged data and ultimately to automate the diagnostic process. The work described in this thesis aims at proposing a framework that includes techniques, formalisms, and algorithms aimed at automating the process of root cause analysis. In particular, this work uses annotated requirement goal models to represent the monitored systems' requirements and runtime behavior. The goal models are used in combination with log data to generate a ranked set of diagnostics that represent the combination of tasks that failed leading to the observed failure. In addition, the framework uses a combination of word-based and topic-based information retrieval techniques to reduce the size of log data by filtering out a subset of log data to facilitate the diagnostic process. The process of log data filtering and reduction is based on goal model annotations and generates a sequence of logical literals that represent the possible systems' observations. A second level of investigation consists of looking for evidence for any malicious (i.e., intentionally caused by a third party) activity leading to task failures. This analysis uses annotated anti-goal models that denote possible actions that can be taken by an external user to threaten a given system task. The framework uses a novel probabilistic approach based on Markov Logic Networks. Our experiments show that our approach improves over existing proposals by handling uncertainty in observations, using natively generated log data, and by providing ranked diagnoses. The proposed framework has been evaluated using a test environment based on commercial off-the-shelf software components, publicly available Java Based ATM machine, and the large publicly available dataset (DARPA 2000).

Context. Software re-engineering has been identified as a business critical activity to improve legacy systems in industries. It is the process of understanding existing software and improving it, for modified or improved functionality, better maintainability, configurability, reusability, or other quality goals. However, there is little knowledge to integrate software quality attributes into the re-engineering process. It is essential to resolve quality problems through applying software re-engineering processes. Objectives. In this study we perform an in-depth investigation to identify and resolve quality problems by applying software re-engineering processes. At the end, we created a quality driven re-engineering framework. Methods. At first, we conducted a literature review to get knowledge for building the quality driven re-engineering framework. After that, we performed a case study in Ericsson Company to validate the processes of the framework. At last, we carried out an experiment to prove that the identified quality problems has been resolved. Results. We compared three existing re-engineering frameworks and identified their weaknesses. In order to fix the weaknesses, we created a quality driven re-engineering framework. This framework is used to improve software quality through identifying and resolving root cause problems in legacy systems. Moreover, we validated the framework for one type of legacy system by successfully applying the framework in a real case in Ericsson Company. And also, we proved that the efficiency of a legacy system is improved after executing an experiment in Ericsson Company. Conclusions. We conclude that the quality driven re-engineering framework is applicable, and it can improve efficiency of a legacy system. Moreover, we conclude that there is a need for further empirical validation of the framework in full scale industrial trials.

Context: Removal of wastes is a crucial area in lean software development. It has been found that there was little evidence on root causes of wastes in lean software development. Root causes from the state of practice had not being investigated. Furthermore, relations between wastes were now successfully exposed through root cause identifications process. Objectives: The objective of this study was to perform an in-depth investigation to identify causes which lead to wastes in Lean software development process in the context of medium to large software development. To this end, researcher also identified relationships that exist between wastes. Methods: The researcher conducted Literature review to look for evidence on waste related activities offered in peer-reviewed literature. Furthermore, the author conducted seven semi-structured interviews and used Grounded Theory method for both literature and interview data analysis. Results: The researcher identified three categories of factors of wastes. Namely, Technical, Non-technical and Global software product development. In the technical category, factors relating to different technical aspects to build a product such as required resource issues, solving complexity issues among others were identified. Similarly, factors relating to people knowledge, management issues as well as factors that bothered on communication, coordination and temporal distance were identified as non-technical and global software product development respectively. For all seven kinds of wastes the root causes were identified. / 0046734784551

Primary objective of this thesis is identify the actual causes of the problem in the organization and subsequently create the most appropriate measures for its elimination. In the first part of thesis basic approaches which deal with changes in organizations are introduced. These approaches are Process Management, Reengineering, Six Sigma, Lean management and Root Cause Analysis. Some of the principles and methods introduce especially in the Root Cause Analysis are then applied during suggestions development in the practical part of the thesis. The second part begins with the introduction of the company and the main problem that the company faces is defined. It is thereafter followed by analysis of the current state which uses a detailed rendering of ongoing processes identifies a critical issue. Based on these findings are diagnosed possible real causes which possible individual solutions are formed. After evaluation of individual proposals that solution is selected which leads to the largest increase in efficiency. In conclusion the specifications of the selected solution are created and evaluated its contributions.

The failure analysis and resolution in cloud-computing environments are a a highly important issue, being their primary motivation the mitigation of the impact of such failures on applications hosted in these environments. Although there are advances in the case of immediate detection of failures, there is a lack of research in root cause analysis of failures in cloud computing. In this process, failures are tracked to analyze their causal factor. This practice allows cloud operators to act on a more effective process in preventing failures, resulting in the number of recurring failures reduction. Although this practice is commonly performed through human intervention, based on the expertise of professionals, the complexity of cloud-computing environments, coupled with the large volume of data generated from log records generated in these environments and the wide interdependence between system components, has turned manual analysis impractical. Therefore, scalable solutions are needed to automate the root cause analysis process in cloud computing environments, allowing the analysis of large data sets with satisfactory performance. Based on these requirements, this thesis presents a data-driven solution for root cause analysis in cloud-computing environments. The proposed solution includes the required functionalities for the collection, processing and analysis of data, as well as a method based on Bayesian Networks for the automatic identification of root causes. The validation of the proposal is accomplished through a proof of concept using OpenStack, a framework for cloud-computing infrastructure, and Hadoop, a framework for distributed processing of large data volumes. The tests presented satisfactory performance, and the developed model correctly classified the root causes with low rate of false positives. / A análise e reparação de falhas em ambientes de computação em nuvem é uma questão amplamente pesquisada, tendo como principal motivação minimizar o impacto que tais falhas podem causar nas aplicações hospedadas nesses ambientes. Embora exista um avanço na área de detecção imediata de falhas, ainda há percalços para realizar a análise de sua causa raiz. Nesse processo, as falhas são rastreadas a fim de analisar o seu fator causal ou seus fatores causais. Essa prática permite que operadores da nuvem possam atuar de modo mais efetivo na prevenção de falhas, reduzindo-se o número de falhas recorrentes. Embora essa prática seja comumente realizada por meio de intervenção humana, com base no expertise dos profissionais, a complexidade dos ambientes de computação em nuvem, somada ao grande volume de dados oriundos de registros de log gerados nesses ambientes e à ampla inter-dependência entre os componentes do sistema tem tornado a análise manual inviável. Por esse motivo, torna-se necessário soluções que permitam automatizar o processo de análise de causa raiz de uma falha ou conjunto de falhas em ambientes de computação em nuvem, e que sejam escaláveis, viabilizando a análise de grande volume de dados com desempenho satisfatório. Com base em tais necessidades, essa dissertação apresenta uma solução guiada por dados para análise de causa raiz em ambientes de computação em nuvem. A solução proposta contempla as funcionalidades necessárias para a aquisição, processamento e análise de dados no diagnóstico de falhas, bem como um método baseado em Redes Bayesianas para a identificação automática de causas raiz de falhas. A validação da proposta é realizada por meio de uma prova de conceito utilizando o OpenStack, um arcabouço para infraestrutura de computação em nuvem, e o Hadoop, um arcabouço para processamento distribuído de grande volume de dados. Os testes apresentaram desempenhos satisfatórios da arquitetura proposta, e o modelo desenvolvido classificou corretamente com baixo número de falsos positivos.

Företag måste hela tiden förbättras vilket ofta görs med ett systematiskt förbättringsarbete. Dessa bygger ofta på data vilket i företag med mycket manuella processer och låg användning av digitala system inte finns. Därför undersöker denna studie hur relevant data ska samlas in av sådana företag. Detta görs i form av en fallstudie på Elitfönster som är en ledande fönstertillverkare, som har en hög grad av omarbete vilket de har en ambition att reducera. Studien resulterar i en instruktion för hur en datainsamling ska genomföras för att generera relevant data till ett förbättringsarbete. Den undersöker även hur grundorsaker till slöserier hittas med hjälp av två olika metoder. Slutsatsen som författarna drog av arbetet blev att planeringen är viktig för att få en fungerande datainsamling som genererar ett bra resultat samt att fallföretaget bör utse ansvariga för de gemensamma resurser som skapar problem i produktionen. / Companies have to continuously improve which is often done through a systematic approach to improvements. This is often based on data which does not excist in companies with a high amount of manual labor and a low use of digital systems. Therefore this report studies how companies as the one previously mentioned should collect data. This is done as a case study at Elitfönster which is a leading window maker, who has a high degree of rework in their process which they aim to reduce. The result of the study is an instruction of how a data collection should be performed to generate relevant data to be able to improve. The study also investigates how root causes to waste are found through two different methods. The conclusion of this study is that the planning of a data collection is important to be able to get a good result. The company should also nominate a department that is responsible for the joint resources which causes problems in the production.

Identifying common root causes of systematic defects in a short time is crucial for yield improvement. Diagnosis driven yield analysis (DDYA) such as Root cause deconvolution (RCD) is a method to estimate root cause distribution by applying statistical analysis on volume diagnosis. By fixing identified common root causes, yield can be improved. With advanced technologies, smaller feature size and more complex fabrication processes for manufacturing VLSI semiconductor devices lead to more complicated failure mechanisms. Lack of domain knowledge of such failure mechanisms makes identifying the emerging root causes more and more difficult. These root causes include but are not limited to layout pattern (certain prone to fail layout shapes) and cell internal root causes. RCD has proved to have certain degree of success in previous work, however, these root cause are not included and pose a challenge for RCD. Furthermore, complex volume diagnosis brings difficulty in investigation on RCD. To overcome the above challenges to RCD, improvement based on better understanding of the method is desired. The first part of this dissertation proposes a card game model to create controllable diagnosis data which can be used to evaluate the effectiveness of DDYA techniques. Generally, each DDYA technique could have its own potential issues, which need to be evaluated for future improvement. However, due to limitation of real diagnosis data, it is difficult to, 1. Obtain diagnosis data with sufficient diversity and 2. Isolate certain issues and evaluate them separately. With card game model given correct statistical model parameters, impact of different diagnosis scenarios on RCD are evaluated. Overfitting problem from limited sample size is alleviated by the proposed cross validation method. In the second part of this dissertation, an enhanced RCD flow based on pre-extract layout patterns is proposed to identify layout pattern root causes. Prone to fail layout patterns are crucial factors for yield loss, but they normally have enormous number of types which impact the effectiveness of RCD. Controlled experiment shows effectiveness of enhanced RCD on both layout pattern root causes and interconnect root causes after extending to layout pattern root causes. Test case from silicon data also validates the proposed flow. The last part of this dissertation addresses RCD extension to cell internal root causes. Due to limitation of domain knowledge in both diagnosis process and defect behavior, parameters of RCD model are not perfectly accurate. As RCD moves to identify cell internal root causes, such limitation become an unescapable challenge for RCD. Due to inherent characteristics of cell internal root cause, RCD including cell internal root cause faces more difficulty due to less accurate model parameters. Rather than enhancing domain knowledge, supervised learning for more accurate parameters based on training data are proposed to improve accuracy of RCD. Both controlled experiments and real silicon data shows that with parameters learned from supervised learning, accuracy of RCD with cell internal root cause are greatly improved.

Aim: The aim of this study was to examine if student nurse participation in root cause analysis has the potential to reduce harm to patients from medication errors by increasing student nurse sensitivity to signal and responder bias. Background: Schools of nursing have traditionally relied on strategies that focus on individual characteristics and responsibility to prevent harm to patients. The modern patient safety movement encourages utilization of systems theory strategies like Root Cause Analysis (RCA). The Patient Risk Detection Theory (Despins, Scott-Cawiezell, & Rouder, 2010) supports the use of nurse training to reduce harm to patients. Method. Descriptive and inferential analyses of the demographic and major study variables were conducted. Validity and reliability assessments for the instruments were performed. The Safe Administration of Medications-Revised Scale (Bravo, 2014) was used to measure sensitivity to signal. The Safety Attitudes Questionnaire (SAQ; Sexton et al., 2006) was used to assess responder bias; this was the first use of this instrument with nursing students. Results: The sample consisted of 125 senior-level nursing students from three universities in the southeastern United States. The SAQ was found to be a valid and reliable test of safety attitudes in nursing students. Further support for the validity and reliability of the SAM-R was provided. A significant difference in safety climate between schools was observed. There were no differences detected between the variables. Conclusion: The results of this study provide support for the use of the SAQ and the SAM-R to further test the PRDT, and to explore methods to improve nursing student ability to administer medications safely.