Behavioral software engineering: A definition and systematic literature review

Lenberg, Per; Feldt, Robert; Wallgren, Lars Göran

doi:10.1016/j.jss.2015.04.084

Cited by 196 publications

(203 citation statements)

References 179 publications

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“…As software development is intellectual and cognitive based (Lenberg et al 2015), we have to limit the developers' context switches and their concentration. Therefore, I will concentrate on non-invasive detection systems.…”

Section: How Can Software Quality Information Needs Be Detected Unobtmentioning

confidence: 99%

Software quality information needs

Graziotin¹

2016

RIO

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Modern software methodologies, e.g., agile and continuous integration and deployment, rely on frequent feedback loops to respond to sudden changes. Improvements to feedback, development activities, and quality rely on information needs. We found a lack of understanding on the information needs of software engineers when the software quality is the main concern. I propose the term "software quality information needs" to refer to a software engineer's eagerness to define, locate, obtain, and use information to satisfy a conscious or unconscious concern towards software quality. I want to understand what information about software quality does a developer need while performing code changes or designing new parts of a system. For understanding the broad research question, I propose three sub-questions, namely (1) How can we conceptualize information needs when dealing with software quality? (2) What information is needed when dealing with software quality? and (3) How can software quality information needs be detected unobtrusively through behavioral patterns? I propose a 24 months plan for a research fellowship comprising of three empirical studies, which will involve 120 students in computer science and the companies Daimler, Porsche, and Bosch. The studies will make use of multidisciplinary research in software engineering and behavioral science. The theoretical results of the execution of this proposal are the software quality information needs theory and instances, which will add to the body of knowledge in software engineering. The practical implications might be large. Providing a software developer with the right kind of information about the current state of and the effect of changes on software quality can prevent catastrophic software failures and avoid opening up security holes. ‡

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Section: How Can Software Quality Information Needs Be Detected Unobtmentioning

confidence: 99%

Software quality information needs

Graziotin¹

2016

RIO

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“…When the systems in use face challenges related to supporting cooperative work, maritime systems developers can only imagine how to solve such problems with the current systems, even without sufficient proof that the solutions will address the safety issues in cooperative work [64]. It is understandable that approaches addressing human-related topics in systems design are rare [34]. Although researchers [9] have stated that the binding of artifacts and locations can help investigate the meanings of computational artifacts in work practices, a "how-to" kit for systems developers is still lacking.…”

Section: Discussionmentioning

confidence: 99%

“…We utilize the concept of 'computational artifact' [32,33] to visualize the ethnographic outcomes from the fieldwork to conduct three workshops with three systems developers in face-to-face sessions. We point out that, although CSCW may have paid less attention to inspiring systems developers in their hands-on work [30,34], our work can be used as a starting point for guiding interactional relationships in real work settings. For example, CSCW researchers can address relations between artifacts and work practices, and combine them as a computational artifact (named 'object' in this paper).…”

Section: Introductionmentioning

confidence: 99%

From Offshore Operation to Onshore Simulator: Using Visualized Ethnographic Outcomes to Work with Systems Developers

Pan

Finken

2018

Informatics

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This paper focuses on the process of translating insights from a Computer Supported Cooperative Work (CSCW)-based study, conducted on a vessel at sea, into a model that can assist systems developers working with simulators, which are used by vessel operators for training purposes on land. That is, the empirical study at sea brought about rich insights into cooperation, which is important for systems developers to know about and consider in their designs. In the paper, we establish a model that primarily consists of a 'computational artifact'. The model is designed to support researchers working with systems developers. Drawing on marine examples, we focus on the translation process and investigate how the model serves to visualize work activities; how it addresses relations between technical and computational artifacts, as well as between functions in technical systems and functionalities in cooperative systems. In turn, we link design back to fieldwork studies.

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“…In another work, software project human error reasons are classified into the attention of humans, communication error, and organization error (Harwood and Sanderson, 1986). The level of communication between stakeholders which is fully related to human personality is another issue in requirement engineering [21].…”

Section: A Human Errorsmentioning

confidence: 99%

Impact Propagation of Human Errors on Software Requirements Volatility

Askarinejadamiri¹,

Ghani²,

Zulzallil³

et al. 2017

ijacsa

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Abstract-Requirements volatility (RV) is one of the key risk sources in software development and maintenance projects because of the frequent changes made to the software. Human faults and errors are major factors contributing to requirement change in software development projects. As such, predicting requirements volatility is a challenge to risk management in the software area. Previous studies only focused on certain aspects of the human error in this area. This study specifically identifies and analyses the impact of human errors on requirements gathering and requirements volatility. It proposes a model based on responses to a survey questionnaire administered to 215 participants who have experience in software requirement gathering. Exploratory factor analysis (EFA) and structural equation modelling (SEM) were used to analyse the correlation of human errors and requirement volatility. The results of the analysis confirm the correlation between human errors and RV. The results show that human actions have a higher impact on RV compared to human perception. The study provides insights into software management to understand socio-technical aspects of requirements volatility in order to control risk management. Human actions and perceptions respectively are a root cause contributing to human errors that lead to RV.

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Behavioral software engineering: A definition and systematic literature review

Cited by 196 publications

References 179 publications

Software quality information needs

Software quality information needs

From Offshore Operation to Onshore Simulator: Using Visualized Ethnographic Outcomes to Work with Systems Developers

Impact Propagation of Human Errors on Software Requirements Volatility

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