Impact Propagation of Human Errors on Software Requirements Volatility

Purpose The purpose of this paper is to examine the effect of team learning processes (information acquisition, information processing and information storage and retrieval) on team performance in software development projects and to assess the effect of ethical leadership and its influence on these processes. Design/methodology/approach The authors examined 354 software development project teams in Thailand. A quantitative study was conducted through a Web-based survey, with structural equation modelling used to test the hypotheses. Findings The results of this study revealed that ethical leadership is positively correlated with the team learning processes of information acquisition, information processing and information storage and retrieval. However, only information processing and information storage and retrieval are positively related to team performance. The results indicate that ethical leadership has a positive direct effect on team performance. The results also show that the positive relationship between ethical leadership and team performance is partially mediated by team learning processes, namely, information processing and information storage and retrieval. Research limitations/implications This study highlights the importance of ethical leadership and guides managers towards considering the characteristics of both ethical leadership and team learning processes for enhancing team performance in software development projects. Originality/value This is the first study to the best of authors’ knowledge to examine the role of team learning processes in mediating the relationship between ethical leadership and team performance, particularly concerning software development projects. The present research contributes to the literature on team performance management, emphasising the manner in which ethical leadership can result in team learning and team performance. The findings of this study can be used to encourage organisations to develop ethical leadership behaviours and team learning processes in software development projects.

Quantification of performance shaping factors (PSFs)’ weightings for human reliability analysis (HRA) of low power and shutdown (LPSD) operations

Background: This Research Category Full Paper presents the results of authors' efforts to develop and evaluate learning materials for introducing Software Engineering (SE) students to the Cognitive Psychology concept of human errors (specifically to those human errors that occur during software development). During the last few years, the authors have developed, through a rigorous literature review and empirical investigation, human error intervention instrumentation and supporting training/teaching material. The intervention instrument consists of a corpus of human errors and a tool to support human error based software requirements inspections. The primary aim of developing this instrumentation and training material is to impart SE/CS students with the knowledge about the most frequently committed human errors during the software development process. Goal and Method: First, a study was conducted with Industry Practitioners with the goal of examining if the practitioners believed that human errors and human error training are useful and relevant to the software development process. Next, based on feedback from the practitioners, a study was conducted in an undergraduate Software Engineering course where students were trained using the human error instrument and were asked to perform error based requirements inspections. The high-level goal of this paper is to evaluate whether requirements inspections supported by human errors can be used to deliver knowledge about software engineering human errors as well as knowledge about requirements inspections (a key industry skill) to students. Results: Results showed that industry practitioners found the human error instrumentation and training useful. Based on their feedback, when the training was administered to students, it helped students understand those human errors that are the frequently committed during the software development process.

Neural Topologies of Reinforcement Sensitivity Theory: A Latent Variable Approach to Magnetic Resonance Imaging Data.

Because the occupational risk expressed the possibility of ending up in a (possible) danger of injury and/or occupational illness, it can represent a criterion of qualitative appreciation of the accomplishment of a human action, indicating how it may produce undesirable effects, materialized by human and material damage. The risk associated with a human action is determined by a multitude of risk factors, both of internal nature (manifested by human error) and of an external nature (technical equipment and related protection systems), which manifest themself in an organizational framework in which the executor carries out his activity, where the problem of risk perception has a dual character: the executor�s own risk versus the risk associated with human error. The human reasoning behind an action is generally based on at the mental level by each performer, the situation of a risky, complex or foreseeable damage action requiring a preliminary assessment of the occupational risk associated with that action. The paper highlights the results obtained based on the assessment of the risk associated with human error in the application of the framework method of exploitation with undermined coal bank, using a grapho-analytical model to estimate and assess the degree of global impairment (the severity of the maximum predictable consequence) and the corresponding probability, as a result of the verifying compliance of the conformity of the technical prescriptions for safety and health at work.

Maturity, models, and goals: How to build a metrics plan

Abstract. Many business‐oriented software applications are subject to frequent changes in requirements. This paper shows that, ceteris paribus, increases in the volatility of system requirements decrease the reliability of software. Further, systems that exhibit high volatility during the development phase are likely to have lower reliability during their operational phase. In addition to the typically higher volatility of requirements, end‐users who specify the requirements of business‐oriented systems are usually less technically oriented than people who specify the requirements of compilers, radar tracking systems or medical equipment. Hence, the characteristics of software reliability problems for business‐oriented systems are likely to differ significantly from those of more technically oriented systems.

 Abstract—This paper explains the roles of key IT Personnel during the development of the Software Requirement Specification (SRS) in the Malaysian Public Sector. We determine the flow of the SRS procedure through the interview process to nine of IT Manager in Malaysian Public Sector. They explain their experience during requirement gathering in IT Project. Our purpose is to standardize their roles and responsibility as a guideline during our software project development. We diagram their experience to be easier understood by IT Personnel. The process flow contents of activity which blends with RE process. We have done the refining using Delphi Technique to the IT Expert in Malaysian Public Sector. We hope that the team will produce the structured SRS for as their guideline for complete the software project development.

Confirmatory Factor Analysis (CFA) has been a popular yet limited approach to assessing latent factor structures. Despite items rarely loading exclusively on one latent factor in multifactorial scales, CFA assumes all indicators/items should load uniquely on their allocated latent dimensions. To address this weakness, Exploratory Structural Equation Modeling (ESEM) combines exploratory factor analyses (EFA) and CFA procedures, allowing cross-loadings to occur when assessing hypothesized models. Although such advantages have enhanced ESEM popularity, its adoption is often limited by software rigidity and complex coding difficulties. To address these obstacles, the current tutorial presents a streamlined, step-by-step approach using the open-source software R while providing both R and Mplus ESEM syntax. The tutorial demonstrates the sequence of the ESEM stages by examining the frequently debated Strengths and Difficulties Questionnaire (SDQ) factor structure, using openly accessible data from the Longitudinal Study of Australian Children (LSAC). As ESEM may allow a better understanding of the complex associations in multidimensional scales, this tutorial may optimize the epidemiological and clinical assessment of common yet multifaceted psychiatric presentations.

Managing software development and maintenance projects requires early knowledge about quality and effort needed for achieving a necessary quality level. Quality prediction models can identify outlying software components that might cause potential quality problems. Quality prediction is based on experience with similar predecessor projects constructing a relationship between the output-usually the number of errors-and some kind of input-here we use complexity metrics-to the quality of a software development project. Two approaches are presented to build quality prediction models: multilinear discriminant analysis as one example for conventional approaches and fuzzy expert-systems generated by genetic algorithms. Using the capability of genetic algorithms, the fuzzy rules can be automatically generated from example data to reduce the cost and improve the accuracy. The generated quality model-with respect to changes-provides both quality of fit (according to past data) and predictive accuracy (according to ongoing projects). The comparison of the approaches gives an answer on the effectiveness and the efficiency of a soft-computing approach.

Every year preventable adverse events endanger a considerable number of patients. Current guidelines of the Federal Joint Committee require clinical quality management to provide amongst others an independent clinical risk management and a critical incident reporting system (CIRS). Such guidelines increase the pressure to actively deal with errors, even in emergency medicine. Human error is considered to be the main cause of preventable adverse events in high-risk industries, such as aviation. This observation is gladly directly transferred to clinical medicine. This study investigated where the true causes for preventable adverse events during the resuscitation of severely injured patients can be found. A non-systematic literature search of the PubMed database was performed. The search identified three recent studies addressing these objectives that revealed human error as the most important cause of preventable adverse events during emergency room resuscitation (88-97%). Errors during resuscitation in the emergency room occur in approximately 10 %. It is striking that such data do not differ greatly from findings described in studies undertaken 20 years ago. One possible explanation might be that the systematic evaluation of medical errors in the emergency room is a weak spot and that too few lessons can be learnt from such incidents. Therefore, this article describes models of error development and outlines methods to collect data for root cause analysis and for clinical risk management. Thus, this review aims at a better understanding of how errors originate and to allow development of strategies to prevent errors from happening again. Human error is the most important cause of preventable adverse events during emergency room resuscitation. Presumably, errors occur unintentionally and as a result of situational misjudgment. As such errors have marked consequences on mortality and morbidity of severely injured patients, an extensive risk management is mandatory for the improvement of quality and safety. Appropriate methods to record errors in order to allow a correct root cause analysis according to well-established protocols is a basic prerequisite.

The growing usage of Agile methodology in software development projects among industry professionals (software engineers, system analysts, requirement engineers, etc.) and academia (software engineering students) leads to the need for the implementation of UML diagram for requirements modelling. Use case diagram, an example of UML diagram, is a very powerful tool to model the requirements specified by the users while also helping the development teams understand the functionalities and interactions between users and the system. However, there is a lack of a system or tool that can perform the operation to visualise the use case diagram directly from user stories because generating this diagram manually requires a deep understanding of the requirements and effective communications with stakeholders and it consume lots of time while previous studies which relate to this study are unable to fulfil the relationship elements of use case diagram. This study will introduce a method to visualise the use case diagram from structured textual user stories by utilising Natural Language Processing (NLP) and application of logical rules which will be done in four stages, namely Requirement Gathering, Natural Language Processing, Application of Logical Rules and UML Diagram Generation. A tool named Stanford CoreNLP will be used to perform four techniques of NLP: tokenisation, stemming and lemmatisation, POS tagging and dependency parsing to process the textual user stories, followed by applying the logical rules before generating the use case diagram. This study will propose a method to solve the gap, which is the problem with the generation of relationship elements, while contribute a semi-automated approach to generate a use case diagram from user stories.

Allen and Meyer's(1990) 3-component model of organizational commitment(OC) was investigated using exploratory structural equation modeling(ESEM) with samples of full-time social workers at social welfare organizations in South Korea. Confirmatory factor analysis(CFA) has been at the heart of testing factor structure of the 3-component model in organizational commitment research wherein each scale of the affective, continuance, and normative commitment is reported to measure conceptually and empirically separable construct. The present study applied ESEM, specifically ‘intra-scale’ and ‘inter-scale’ ESEM, to scrutinize factor structure of the 3-component scales. ESEM methodology uses exploratory approach in that all cross-loadings are estimated between each measure and factors, with uniqueness correlated according to the researcher's hypotheses as in CFA. In this respect, ESEM can be viewed as an open approach to item analysis distinguished from the conventional (closed) approach, such as EFA and CFA. This study provided detailed assessment of the 3-component model through comparisons of factor structures estimated by EFA, CFA and ‘intra’ ESEM, followed by the ‘inter’ ESEM conducted on all other variables(assumed to be similar constructs with or antecedents of OC). As a result, the ‘intra-scale’ ESEM showed a substantially better fit and yielded more discriminated factors(less correlated) than did EFA and CFA that are models for planned scale. The ‘inter-scale’ ESEM revealed how seriously method effect can distort an original factor structure in empirical data measured together with multiple scales of other constructs. Using ESEM has advantages of estimating common factor structures, controlling for common method effect that are typically included in measures in applied research. Also, it allows for much more possibilities that each item can measure multiple constructs so as to reveal more realistic factor structures. Taken together, the present results suggest a need to conceptualize and validate a new scale for organizational commitment reflecting Korean culture.

This study offers methodological synergy in the examination of factorial structure in second language (L2) research. It illustrates the effectiveness and flexibility of the recently developed exploratory structural equation modeling (ESEM) method, which integrates the advantages of exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) into one complete measurement model. Two sets of data were collected using the L2 Passion Scale, which measures a dualistic model of passion. Study 1 participants were 220 L2 students. A comparison was made between the CFA and the ESEM models. The results demonstrated the superiority of the ESEM method relative to CFA in terms of better goodness-of-fit indices and realistic correlated factors. These results were replicated in another sample of 272 L2 students, providing support for the predictive validity using a structural ESEM model. Guidelines are provided and Mplus syntax files (codes) are included to help analysts apply the methods. We also make the data available publicly. Overall, this research demonstrated the usefulness of ESEM for examining the construct, discriminant, and convergent validity of L2 scales over CFA.

Equations for the Edgeworth expansion of the distributions of the estimators in exploratory factor analysis and structural equation modeling are given. The equations cover the cases of non-normal data, as well as normal ones with and without known first-order asymptotic standard errors. When the standard errors are unknown, the distributions of the Studentized statistics are expanded. Methods of constructing confidence intervals of population parameters with arbitrary asymptotic confidence coefficients are given using the Cornish-Fisher expansion. Simulations are performed to see the usefulness of the asymptotic expansions in exploratory factor analysis with rotated solutions and confirmatory factor analysis. The results show that asymptotic expansion gives substantial improvement of approximation to the exact distribution constructed by simulations over the usual normal approximation.