Smart Grids and Software Testing Process Models

Hrabovská, Katarína; Šimková, Nikola; Rossi, Bruno; Pitner, Tomáš

doi:10.1109/scsp.2019.8805718

Cited by 3 publications

(3 citation statements)

References 40 publications

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“…This leaves the need for secondary studies in the areas of test‐environment development and setup, test results evaluation and reporting’. The same type of criticism also exists for software testing models such as TMMi (Test Maturity Model integration), for example, Hrabovská et al [20] state that a problem with TMMi is the ‘lack of important key areas such as Test Environment’.…”

Section: Reviewing Literaturementioning

confidence: 99%

“…A similar (but only partly overlapping) summary from Ramler and Gmeiner [22] includes experiences from four challenges related to test environments for automating testing: the right test environments available on time and on budget, test environments for products including hardware and software systems, software and hardware variants and support for automated testing. In the literature review, we also found that many publications only provide generic and shallow recommendations, such as ‘test environment has to be organized and available for testing when needed’ [20].…”

Section: Reviewing Literaturementioning

confidence: 99%

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Test environments for large‐scale software systems—An industrial study of intrinsic and extrinsic success factors

Mårtensson

Ancher

Ståhl

2023

Software Testing Verif & Rel

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The characteristics of the test environment are of vital importance to its ability to support the organizations testing objectives. This paper seeks to address the need for a structured and reliable approach, which can be used by companies and other organizations to optimize their test environments in each individual case. The reported study included a series of interviews with 30 individuals, a series of focus groups with in total 31 individuals and a cross‐company workshop with 30 participants from five large‐scale companies, operating in different industry segments. The study resulted in a list of success factors, including not only characteristics and capabilities existing within a test environment (intrinsic success factors) but also properties not inherent to the test environment, but still vital for a successfully implemented test environment (extrinsic success factors). This distinction is important, as the root causes differ and as addressing them requires distinct approaches—not only of technology but also of organization, communication and collaboration. We find that successful implementations of test environments for large‐scale software systems depend primarily on how they support the company's business strategy, test organization and product testability (extrinsic success factors). Based on this, test environments can then be optimized to improve test environment capabilities, usability and stability (intrinsic success factors). The list of intrinsic and extrinsic success factors was well received by all five companies included in the study, supporting that the intrinsic and extrinsic success factors for test environments can be applied to a large segment of the software industry.

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Section: Reviewing Literaturementioning

confidence: 99%

Section: Reviewing Literaturementioning

confidence: 99%

Test environments for large‐scale software systems—An industrial study of intrinsic and extrinsic success factors

Mårtensson

Ancher

Ståhl

2023

Software Testing Verif & Rel

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“…In this section, we propose our big data platform for smart meters power consumption anomaly detection, based on our previous research ( [29], [8], [30], [31]). The goal of such platform is to process large amounts of data from smart meters and weather information sources to detect anomalous behaviours from the side of customers.…”

Section: Proposed Platformmentioning

confidence: 99%

Big Data Platform for Smart Grids Power Consumption Anomaly Detection

Lipcak¹,

Macák²,

Rossi³

2019

Annals of Computer Science and Information Systems

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Big data processing in the Smart Grid context has many large-scale applications that require real-time data analysis (e.g., intrusion and data injection attacks detection, electric device health monitoring). In this paper, we present a big data platform for anomaly detection of power consumption data. The platform is based on an ingestion layer with data densification options, Apache Flink as part of the speed layer and HDFS/KairosDB as data storage layers. We showcase the application of the platform to a scenario of power consumption anomaly detection, benchmarking different alternative frameworks used at the speed layer level (Flink, Storm, Spark).

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