Practitioners’ Perspectives on Change Impact Analysis for Safety-Critical Software – A Preliminary Analysis

Borg, Markus; Wnuk, Krzysztof

doi:10.1007/978-3-319-45480-1_28

Cited by 13 publications

(6 citation statements)

References 13 publications

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“…We conduct a case study with two units of analysis on software engineers' information seeking in CIA. This contribution is part of an ongoing series of works to support CIA in a safety-critical context, thus it should be interpreted along with our previous publications 1 [11], [10]. We show that engineers seek information in several ways, including searching and browsing databases, asking colleagues, and consulting formal trace links.…”

Section: Introductionmentioning

confidence: 65%

Software Engineers' Information Seeking Behavior in Change Impact Analysis - An Interview Study

Borg

Alégroth

Runeson

2017

2017 IEEE/ACM 25th International Conference on Program Comprehension (ICPC)

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Software engineers working in large projects must navigate complex information landscapes. Change Impact Analysis (CIA) is a task that relies on engineers' successful information seeking in databases storing, e.g., source code, requirements, design descriptions, and test case specifications. Several previous approaches to support information seeking are task-specific, thus understanding engineers' seeking behavior in specific tasks is fundamental. We present an industrial case study on how engineers seek information in CIA, with a particular focus on traceability and development artifacts that are not source code. We show that engineers have different information seeking behavior, and that some do not consider traceability particularly useful when conducting CIA. Furthermore, we observe a tendency for engineers to prefer less rigid types of support rather than formal approaches, i.e., engineers value support that allows flexibility in how to practically conduct CIA. Finally, due to diverse information seeking behavior, we argue that future CIA support should embrace individual preferences to identify change impact by empowering several seeking alternatives, including searching, browsing, and tracing.

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Section: Introductionmentioning

confidence: 65%

Software Engineers' Information Seeking Behavior in Change Impact Analysis - An Interview Study

Borg

Alégroth

Runeson

2017

2017 IEEE/ACM 25th International Conference on Program Comprehension (ICPC)

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“…Borg et al [25] and De la Vara et al [26] clarify finding F3 at least for the specific case of change impact analysis in safety practice.…”

Section: Relation To Existing Evidencementioning

confidence: 95%

“…specifications, issue databases, designs) are propagated and whether these changes have negative safety impact. Borg et al [25] report on 14 interviews with SPs about their CIA activities, finding that SPs have difficulties in understanding the motivation of CIA, are overwhelmed by the information they have to process when conducting CIA, and struggle with trusting and updating former CIAs. From a cross-sectional survey of 97 practitioners, De la Vara et al [26] observe insufficient CIA tool support.…”

Section: Adequacy Of Means Of Work In Safetymentioning

confidence: 99%

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Safety Practice and its Practitioners: Exploring a Diverse Profession

Gleirscher,

Nyokabi

2018

Preprint

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Context: System safety refers to a diverse engineering discipline assessing and improving various aspects of safety in socio-technical systems and their software-intensive sub-systems. While system safety has been a vital area of applied research for many decades, its practice and practitioners seem empirically still not well studied. Beyond anecdotal evidence-case reports, interviews, discussion forums, blogs, and "war stories" serving as interesting examples in textbooks-and surveys, we are missing open, large-scale, and longterm empirical investigations that promote knowledge transfer and research validation. Objective: In this article, we explore means for work that safety practitioners rely on, factors influencing the performance of these professionals, and their perception of their role in the system life cycle. Along with that we want to examine observations from previous research. Methods: We build a construct of safety practice, collect data for this construct using an on-line survey, summarise and interpret the collected data, and investigate several hypotheses based on the previous observations. Results: We analyse and present the responses of 124 practitioners in safetycritical system and software projects. Aside from other findings, our data • suggests that safety decision making mainly depends on expert opinion and project memory, • lacks evidence that safety is typically a cost-benefit question, • does not exhibit the prejudice that formal methods are not beneficial, • leaves it unclear as to whether or not standards and methods have become inadequate, and • indicates that safety is not typically confused with reliability. Additionally, we contribute a research design directing towards explanatory empirical studies of safety practice. Conclusions: We believe that empirical research of safety practice is still in an early stage, bearing the risk of undesirable mismatches of the state of the art and the state of practice. However, this situation offers great opportunities for research.

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“…The monitoring and alerting of changes to the procedures within these systems is of great importance to ensure no alterations made during system maintenance interfere with critical functions. Examples where CD has been implemented include clinical decision support systems [ 26 ], web ontologies [ 24 ] and safety-critical software [ 27 ].…”

Section: Applications Of Change Detectionmentioning

confidence: 99%

Representation Learning for Fine-Grained Change Detection

Mahony

Campbell

Krpálková

et al. 2021

Sensors

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Fine-grained change detection in sensor data is very challenging for artificial intelligence though it is critically important in practice. It is the process of identifying differences in the state of an object or phenomenon where the differences are class-specific and are difficult to generalise. As a result, many recent technologies that leverage big data and deep learning struggle with this task. This review focuses on the state-of-the-art methods, applications, and challenges of representation learning for fine-grained change detection. Our research focuses on methods of harnessing the latent metric space of representation learning techniques as an interim output for hybrid human-machine intelligence. We review methods for transforming and projecting embedding space such that significant changes can be communicated more effectively and a more comprehensive interpretation of underlying relationships in sensor data is facilitated. We conduct this research in our work towards developing a method for aligning the axes of latent embedding space with meaningful real-world metrics so that the reasoning behind the detection of change in relation to past observations may be revealed and adjusted. This is an important topic in many fields concerned with producing more meaningful and explainable outputs from deep learning and also for providing means for knowledge injection and model calibration in order to maintain user confidence.

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Practitioners’ Perspectives on Change Impact Analysis for Safety-Critical Software – A Preliminary Analysis

Cited by 13 publications

References 13 publications

Software Engineers' Information Seeking Behavior in Change Impact Analysis - An Interview Study

Software Engineers' Information Seeking Behavior in Change Impact Analysis - An Interview Study

Safety Practice and its Practitioners: Exploring a Diverse Profession

Representation Learning for Fine-Grained Change Detection

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