Software Testing: A Changing Career

Cunningham, Sean; Gambo, Jemil; Lawless, Aidan; Moore, Declan; Yılmaz, Murat; Clarke, Paul M.; O’Connor, Rory V.

doi:10.1007/978-3-030-28005-5_57

Cited by 7 publications

(5 citation statements)

References 32 publications

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“…Software engineering is a complex undertaking [59] that should take account of various situational context considerations [60]. Roles such as Software Engineer and Software Tester have been evolving over time in response to changing contexts [61,62]. The onset of COVID-19 related restrictions has brought about a sudden and highly disruptive change in context and to work patterns for many workers.…”

Section: Discussionmentioning

confidence: 99%

To Work from Home (WFH) or Not to Work from Home? Lessons Learned by Software Engineers During the COVID-19 Pandemic

Nolan

White

Soomro

et al. 2021

Communications in Computer and Information Science

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This research investigates software engineering during the COVID-19 pandemic with a focus on the lessons learned and predictions for future software engineering work. Four themes are explored: Remote work, Team management, Work/Life balance, and Technology/Software Engineering Methods. Our research has demonstrated that software companies will derive tangible benefits from supporting their employees during this uncertain time through ergonomic home offices, listening to their concerns, as well as encouraging breaks and hard stops to boost long term well-being and productivity. It shows that communication and collaboration tools, critical to project success, have been utilised. The hiring of new talent has been reimagined, with managers playing a vital role in the process. The insights gained are significant as they will assuage some pre-existing concerns regarding remote work, creating a new understanding of its role in the future. Looking to a post-COVID-19 future, we envision the rise of hybrid software development working arrangements, with a focus on the Working-From-Home to Not-Working-From-Home ratio -WFH : NWFH -perhaps colloquialised as Home : Not Home (HNH). For many this ratio will be neither 100:0 or 0:100, the former would lead to team breakdowns, developer isolation, difficulties onboarding and too many communication gaps, the latter would lead to disaffected employees. We identify plausible future software engineering working arrangements, noting that there are challenging times ahead for employers and employees as they navigate this HNH future, but there are benefits for both parties in getting the balance right.

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Section: Discussionmentioning

confidence: 99%

To Work from Home (WFH) or Not to Work from Home? Lessons Learned by Software Engineers During the COVID-19 Pandemic

Nolan

White

Soomro

et al. 2021

Communications in Computer and Information Science

Self Cite

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“…There are many other changes that will result from the adoption of FaaS, the role and process adopted by software testers will change, in previous work we have already noted that this has changed significantly in recent years [50]. The space that testers have traditionally occupied between developers and operations becomes less distinct and may fundamentally reshape the role of software test.…”

Section: Discussionmentioning

confidence: 99%

A Multivocal Literature Review of Function-as-a-Service (FaaS) Infrastructures and Implications for Software Developers

Grogan

Mulready

McDermott

et al. 2020

Communications in Computer and Information Science

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In this paper, we provide an analysis of Function as a Service (FaaS) infrastructures. FaaS is an important, emerging category of cloud computing, which requires that software applications are designed and deployed using distributed, highly-decoupled service-based architectures, one example of which is the microservices architecture paradigm. FaaS is associated with on-demand functionality and allows developers to build applications without the overhead associated with server management. As such, FaaS is a type of serverless provisioning model wherein a provider dynamically manages and allocates machine resources, with the developers deploying source code into a production environment. This research provides an analysis of scalability, cost, execution times, integration support, and the constraints associated with FaaS services provided by several vendors: AWS Lambda, Google Cloud Functions, and Azure Functions. We discuss the implications of the findings for software developers.

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“…Traditional software testing involves significant time, resources, and effort [72]; even moderatesized software projects spend hundreds of person-hours writing test cases, implementing them, and meticulously documenting the test results. In fact, software testing is sometimes considered an art [120] requiring its own technical and non-technical skills [112,148], and entire career paths are built around testing [42]. Test-driven development, often associated with agile software engineering frameworks, integrates testing considerations in all parts of the development process [8,65].…”

Section: Evaluation-driven ML Methodologiesmentioning

confidence: 99%

Evaluation Gaps in Machine Learning Practice

Hutchinson¹,

Rostamzadeh²,

Greer³

et al. 2022

Preprint

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Forming a reliable judgement of a machine learning (ML) model's appropriateness for an application ecosystem is critical for its responsible use, and requires considering a broad range of factors including harms, benefits, and responsibilities. In practice, however, evaluations of ML models frequently focus on only a narrow range of decontextualized predictive behaviours. We examine the evaluation gaps between the idealized breadth of evaluation concerns and the observed narrow focus of actual evaluations. Through an empirical study of papers from recent high-profile conferences in the Computer Vision and Natural Language Processing communities, we demonstrate a general focus on a handful of evaluation methods. By considering the metrics and test data distributions used in these methods, we draw attention to which properties of models are centered in the field, revealing the properties that are frequently neglected or sidelined during evaluation. By studying these properties, we demonstrate the machine learning discipline's implicit assumption of a range of commitments which have normative impacts; these include commitments to consequentialism, abstractability from context, the quantifiability of impacts, the limited role of model inputs in evaluation, and the equivalence of different failure modes. Shedding light on these assumptions enables us to question their appropriateness for ML system contexts, pointing the way towards more contextualized evaluation methodologies for robustly examining the trustworthiness of ML models.CCS Concepts: • Computing methodologies → Machine learning approaches.

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Software Testing: A Changing Career

Cited by 7 publications

References 32 publications

To Work from Home (WFH) or Not to Work from Home? Lessons Learned by Software Engineers During the COVID-19 Pandemic

To Work from Home (WFH) or Not to Work from Home? Lessons Learned by Software Engineers During the COVID-19 Pandemic

A Multivocal Literature Review of Function-as-a-Service (FaaS) Infrastructures and Implications for Software Developers

Evaluation Gaps in Machine Learning Practice

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