Predicting Software Defects Based on Cognitive Error Theories

As the primary cause of software defects, human error is the key to understanding and perhaps to predicting and preventing software defects. However, little research has been done to forecast software defects based on defects' cognitive nature. This paper proposes an idea for predicting software defects by applying the current scientific understanding of human error mechanisms. This new prediction method is based on the main causal mechanism underlying software defects: an error-prone scenario triggers a sequence of human error modes. Preliminary evidence for supporting this idea is presented

Human Error Analysis in Software Engineering

As the primary cause of software defects, human error is the key to understanding, detecting and preventing software defects. This chapter first reviews the state of art of an emerging area: software fault defense based on human error mechanisms. Then, an approach for human error analysis (HEA) is proposed. HEA consists of two important components: human error modes (HEM) and an undated version of causal mechanism graphs (CMGs). Human error modes are the general erroneous patterns that humans tend to behave in a variety of activities. Causal mechanism graph provides a way to extract the error-prone contexts in software development, and link the contexts to general human error modes. HEA can be used at various phases of software development, for both defect detection and prevention purposes. An application case is provided to demonstrate how to use HEA

Software reliability and dependability: a roadmap

Shifting the focus from software reliability to user-centred measures of dependability in complete software-based systems. Influencing design practice to facilitate dependability assessment. Propagating awareness of dependability issues and the use of existing, useful methods. Injecting some rigour in the use of process-related evidence for dependability assessment. Better understanding issues of diversity and variation as drivers of dependability. Bev Littlewood is founder-Director of the Centre for Software Reliability, and Professor of Software Engineering at City University, London. Prof Littlewood has worked for many years on problems associated with the modelling and evaluation of the dependability of software-based systems; he has published many papers in international journals and conference proceedings and has edited several books. Much of this work has been carried out in collaborative projects, including the successful EC-funded projects SHIP, PDCS, PDCS2, DeVa. He has been employed as a consultant t

Modeling Human Aspects to Enhance Software Quality Management

The aim of the research is to explore the impact of cognitive biases and social networks in testing and developing software. The research will aim to address two critical areas: i) to predict defective parts of the software, ii) to determine the right person to test the defective parts of the software. Every phase in software development requires analytical problem solving skills. Moreover, using everyday life heuristics instead of laws of logic and mathematics may affect quality of the software product in an undesirable manner. The proposed research aims to understand how mind works in solving problems. People also work in teams in software development that their social interactions in solving a problem may affect the quality of the product. The proposed research also aims to model the social network structure of testers and developers to understand their impact on software quality and defect prediction performance

QUALITY MANAGEMENT IN A CHANGING ORGANIZATIONAL ENVIRONMENT: LOOKING FOR NEW CONVERSATION TOOLS

Teaching/Communication/Extension/Profession,

Anosognosia for hemiplegia as a tripartite disconnection syndrome

© 2019 Pacella et al. This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.The syndrome of Anosognosia for Hemiplegia (AHP) can provide unique insights into the neurocognitive processes of motor awareness. Yet, prior studies have only explored predominately discreet lesions. Using advanced structural neuroimaging methods in 174 patients with a right-hemisphere stroke, we were able to identify three neural systems that contribute to AHP, when disconnected or directly damaged: the (i) premotor loop (ii) limbic system, and (iii) ventral attentional network. Our results suggest that human motor awareness is contingent on the joint contribution of these three systems.Peer reviewedFinal Published versio

a research program

Most process research relies heavily on the use of terms and concepts whose validity depends on a variety of assumptions to be met. As it is difficult to guarantee that they are met, such work continually runs the risk of being invalid. We propose a different and complementary approach to understanding process: Perform all description bottom-up and based on hard data alone. We call the approach actual process and the data actual events. Actual events can be measured automatically. This paper describes what has been done in this area already and what are the core problems to be solved in the future

Predicting human cooperation in the Prisoner’s Dilemma using case-based decision theory

In this paper, we show that Case-based decision theory, proposed by Gilboa and Schmeidler (Q J Econ 110(3):605–639, 1995), can explain the aggregate dynamics of cooperation in the repeated Prisoner’s Dilemma, as observed in the experiments performed by Camera and Casari (Am Econ Rev 99:979–1005, 2009). Moreover, we find CBDT provides a better fit to the dynamics of cooperation than does the existing Probit model, which is the first time such a result has been found. We also find that humans aspire to a payoff above the mutual defection outcome but below the mutual cooperation outcome, which suggests they hope, but are not confident, that cooperation can be achieved. Finally, our best-fitting parameters suggest that circumstances with more details are easier to recall. We make a prediction for future experiments: if the repeated PD were run for more periods, then we would be begin to see an increase in cooperation, most dramatically in the second treatment, where history is observed but identities are not. This is the first application of Case-based decision theory to a strategic context and the first empirical test of CBDT in such a context. It is also the first application of bootstrapped standard errors to an agent-based model

A novel approach towards usability studies for visual search tasks in graphical user interface applications using the activity theory approach

The field of Human Computer Interaction still strives for a generalized model of visual search tasks (icon search, menu search, text search, label search, search through hypertext and feature recognition). The existing models of visual search, in spite of being impressive, are limited under certain perspectives due to lack of generality. The thesis tries to provide a holistic approach for the modeling of visual search tasks in graphical user interfaces from the Activity Theory (AT) perspective with the aim of rendering a theoretical bridge between HCI and Psychology. A detailed review of literature from the variegated discipline contributing to the study of Visual Search revealed the presence of gray areas, which can be partially addressed by the Activity Theory approach. The case study uses thinking aloud Protocol Analysis technique for analyzing the complex interaction of behavior, cognition and motor action, which manifest in these tasks. The results have been analyzed and possible modifications have been identified. Interestingly, it is observed that Activity Theory can provide substantial theoretical support to aid Usability Testing Techniques