| 1. |
- Alégroth, Emil, 1984-, et al.
(författare)
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Practitioners' best practices to Adopt, Use or Abandon Model-based Testing with Graphical models for Software-intensive Systems
- 2022
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Ingår i: Empirical Software Engineering. - : SPRINGER. - 1382-3256 .- 1573-7616. ; 27:5
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Tidskriftsartikel (refereegranskat)abstract
- Model-based testing (MBT) has been extensively researched for software-intensive systems but, despite the academic interest, adoption of the technique in industry has been sparse. This phenomenon has been observed by our industrial partners for MBT with graphical models. They perceive one cause to be a lack of evidence-based MBT guidelines that, in addition to technical guidelines, also take non-technical aspects into account. This hypothesis is supported by a lack of such guidelines in the literature. Objective: The objective of this study is to elicit, and synthesize, MBT experts' best practices for MBT with graphical models. The results aim to give guidance to practitioners and aspire to give researchers new insights to inspire future research. Method: An interview survey is conducted using deep, semi-structured, interviews with an international sample of 17 MBT experts, in different roles, from software industry. Interview results are synthesised through semantic equivalence analysis and verified by MBT experts from industrial practice. Results: 13 synthesised conclusions are drawn from which 23 best-practice guidelines are derived for the adoption, use and abandonment of the technique. In addition, observations and expert insights are discussed that help explain the lack of wide-spread adoption of MBT with graphical models in industrial practice. Conclusions: Several technical aspects of MBT are covered by the results as well as conclusions that cover process- and organizational factors. These factors relate to the mindset, knowledge, organization, mandate and resources that enable the technique to be used effectively within an organization. The guidelines presented in this work complement existing knowledge and, as a primary objective, provide guidance for industrial practitioners to better succeed with MBT with graphical models.
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| 2. |
- Källström, Elisabeth, et al.
(författare)
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On-board Clutch Slippage Detection and Diagnosis in Heavy Duty Machine
- 2018
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Ingår i: International Journal of Prognostics and Health Management. - : PHM Society. - 2153-2648. ; 9:1
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Tidskriftsartikel (refereegranskat)abstract
- In order to reduce unnecessary stops and expensive downtime originating from clutch failure of construction equipment machines; adequate real time sensor data measured on the machinein combination with feature extraction and classification methods may be utilized.This paper, based on a study at Volvo Construction Equipment,presents a framework with feature extraction methods and an anomaly detection module combined with Case-Based Reasoning (CBR) for on-board clutch slippage detection and diagnosis in a heavy duty equipment. The feature extraction methods used are Moving Average Square Value Filtering (MASVF) and a measure of the fourth order statistical properties of the signals implemented as continuous queries over data streams. The anomaly detection module has two components,the Gaussian Mixture Model (GMM) and the Logistics Regression classifier. CBR is a learning approach that classifies faults by creating a new solution for a new fault case from the solution of the previous fault cases. Through use of a data stream management system and continuous queries (CQs), the anomaly detection module continuously waits for a clutch slippage event detected by the feature extraction methods, the query returns a set of features which activates the anomaly detection module. The first component of the anomaly detection module trains a GMM to extracted features while the second component uses a Logistic Regression classifier for classifying normal and anomalous data. When an anomalyis detected, the Case-Based diagnosis module is activated for fault severity estimation.
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