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This paper presents an interpretable machinelearning model for anomaly detection in door locks using torque data. The model aims to replace the human tactile sense in the quality control process, reducing repetitive tasks and improving reliability. The model achieved an accuracy of 96%, however, to gain social acceptance and operators' trust, interpretability of the model is crucial. The purpose of this study was to evaluate anapproach that can improve interpretability of anomalousclassifications obtained from an anomaly detection model. Weevaluate four instance-based counterfactual explanators, three of which, employ optimization techniques and one uses, a less complex, weighted nearest neighbor approach, which serve as ourbaseline. The former approaches, leverage a latent representation of the data, using a weighted principal component analysis, improving plausibility of the counter factual explanations andreduces computational cost. The explanations are presentedtogether with the 5-50-95th percentile range of the training data, acting as a frame of reference to improve interpretability. All approaches successfully presented valid and plausible counterfactual explanations. However, instance-based approachesemploying optimization techniques yielded explanations withgreater similarity to the observations and was therefore concluded to be preferable despite the higher execution times (4-16s) compared to the baseline approach (0.1s). The findings of this study hold significant value for the lock industry and can potentially be extended to other industrial settings using timeseries data, serving as a valuable point of departure for further research., In press

This paper presents an extended version of Deeper, a search-based simulation-integrated test solution that generates failure-revealing test scenarios for testing a deep neural network-based lane-keeping system. In the newly proposed version, we utilize a new set of bio-inspired search algorithms, genetic algorithm (GA), (Formula presented.) and (Formula presented.) evolution strategies (ES), and particle swarm optimization (PSO), that leverage a quality population seed and domain-specific crossover and mutation operations tailored for the presentation model used for modeling the test scenarios. In order to demonstrate the capabilities of the new test generators within Deeper, we carry out an empirical evaluation and comparison with regard to the results of five participating tools in the cyber-physical systems testing competition at SBST 2021. Our evaluation shows the newly proposed test generators in Deeper not only represent a considerable improvement on the previous version but also prove to be effective and efficient in provoking a considerable number of diverse failure-revealing test scenarios for testing an ML-driven lane-keeping system. They can trigger several failures while promoting test scenario diversity, under a limited test time budget, high target failure severity, and strict speed limit constraints., Correspondence Address: M.H. Moghadam; Smart Industrial Automation, RISE Research Institutes of Sweden, Västerås, Stora Gatan 36, 722 12, Sweden; This work has been funded by Vinnova through the ITEA3 European IVVES ( https://itea3.org/project/ivves.html ) and H2020‐ECSEL European AIDOaRT ( https://www.aidoart.eu/ ) and InSecTT ( https://www.insectt.eu/ ) projects. Furthermore, the project received partially financial support from the SMILE III project financed by Vinnova, FFI, Fordonsstrategisk forskning och innovation under the grant number: 2019‐05871.

Quarry sites are complex systems that involve several heavy machines, equipment, people, and management systems working together in an unstructured off-road environment. Gaining accurate insights about these sites requires integrating models at various levels to enable a holistic view systems and processes involved and facilitate effective planning, coordination, and decision-making. In this paper, a multi-level modelling framework is proposed to provide an overall structure for the modelling of quarry sites. The motivation for this framework is drawn from insights gained through a large manufacturing company in the heavy-duty vehicle industry, providing a practical perspective on the modeling approach. The framework integrates models of different operations on site enabling effective simulation and optimization and leading to better understanding of the workflow on site and pointing out any possible bottlenecks. The feasibility of the proposed framework was validated through workshops that included a panel of experts in different areas of the field of off-road machinery production company.

Der Trend zu kleinteiligeren und kapitalintensiveren Transportgütern führt in Kombination mit der in Europa angestrebten Reduktion von Treibhausgasemissionen im Transportsektor zu einer Zunahme der Bedeutung von effizienten Konsolidierungsnetzwerken des Schienengüterverkehrs. Zugehörige Produktionsformen mit Bündelung von Warenströmen wie der Einzelwagenverkehr und der intermodale Verkehr sind somit erfolgskritisch für die Zukunftsfähigkeit des Schienengüterverkehrs. Deren Wettbewerbsfähigkeit kann durch die Einführung und Nutzung von Innovationen gestärkt werden. Beispiele hierfür sind eine Digitale Automatische Kupplung (DAK) sowie Sensoren an Güterwagen und Lokomotiven. Diese Innovationen werden oftmals von einem hohen monetären Aufwand sowie Unsicherheiten hinsichtlich ihrer genauen betrieblichen Wirkung in den Netzwerken begleitet. Für strategische Entscheidungen hinsichtlich einer Einführung solcher Innovationen sind die ökonomischen und betrieblichen Effekte für gezielte Nutzen-Kosten-Betrachtungen aufzuzeigen sowie mögliche Pfade für eine Migration der jeweiligen Innovation für die Produktionsformen und deren zugehörigen Netzwerke zu eruieren. Dabei sind insbesondere die Veränderungen im sogenannten Service Network Design (SND) von großer Bedeutung. Das SND ist Teil der taktischen Netzwerkplanung und definiert das Zuggerüst sowie die Wagenroutenplanung im Netzwerk. Dabei werden die Kosten für den Betrieb von Netzwerken unter Einhaltung definierter Qualitätsstandards minimiert. Das Ergebnis des SND stellt den Rahmen für konkrete Wagenrouten in der betrieblichen Durchführung dar und definiert das zu behandelnde Zuggerüst in den Bündelungsknoten der Netzwerke. Trotz der wichtigen Funktion des SND, ist dieser taktische Planungsprozess in der Praxis noch stark manuell geprägt und daher zeitaufwändig. Außerdem liefert er oft zu ungenaue Aussagen. Das trifft insbesondere auf Aussagen zu den Netzeffekten durch Innovationen zu. Aufgrund der hohen Komplexität von Kons, The trend towards small-scale and more capital-intensive transport goods means that the importance of rail freight consolidation networks is increasing. Production forms such as single wagonload transport and intermodal transport are therefore critical for the future potential of rail freight. The competitiveness of rail freight networks can be strengthened through the introduction of innovations such as a Digital Automatic Coupling (DAC) and sensors on freight wagons and locomotives. These innovations are often accompanied by high investment costs and uncertainties regarding their specific operational impact in the networks. For strategic decisions regarding the introduction of such innovations, the economic and operational effects must be identified and quantified, e.g., for benefit-cost considerations. In this context, the changes in the so-called Service Network Design (SND) are of particular importance. The SND determines train service structures and the freight distribution in networks at the tactical planning level. The number and schedule of operated train services in the network significantly determines the costs and quality of consolidation networks in rail freight. It provides the framework for specific railcar routings at the operational level and defines the number of trains to be handled in the bundling nodes of the networks. Despite this outstanding importance of the SND, the tactical planning of consolidation networks is still a manual process in practice lacking computer-based decision support. This is particularly true for statements on network effects of innovations. Due to the high complexity of consolidation networks in rail freight transport and the lack of computer-based decision support, analyses regarding the effects of innovations in service networks are mainly based on expert interviews in the status quo. Especially for innovations whose migration is associated with extensive process transformations and new operating strategies in the netw

Performance aspects such as travel time, punctuality and robustness are conflicting goals of utmost importance for railway transports. To successfully plan railway traffic, it is therefore important to strike a balance between planned travel times and expected delays. In railway operations research, a lot of attention has been given to construct models and methods to generate robust timetables—that is, timetables with the potential to withstand design errors, incorrect data, and minor everyday disturbances. Despite this, the current state-of-practice in railway planning is to construct timetables manually, possibly with support of microsimulation for robustness evaluation. This paper aims to narrow the gap between the state-of-the-art optimization-based research approaches, and the current state-of-practice to construct timetables by combining simulation and optimization. The paper proposes a combined simulation-optimization approach for double-track lines, which generalizes previous work to allow full flexibility in the order of trains by including a new and more generic model to predict delays. By utilizing delay data from simulation, the approach can make socio-economically optimal modifications of a given timetable by minimizing predicted disutility—the weighted sum of scheduled travel time and total predicted delay. In a large simulation experiment on the heavily congested Swedish Western Main Line, it is demonstrated that compared with a real-life, manually constructed, timetable large reductions of delays as well as improvements in punctuality could be obtained to a small cost of marginally longer travel times. The cost of scheduled in-vehicle travel time and mean delay was reduced by 5% on average, representing a large improvement for a highly utilized railway line. Furthermore, a separate scaling experiment indicate that the approach can be suitable also for larger problems., QC 20231215

Punctuality is commonly recognized as one of the most important quality indicators for passenger traffic. Despite this, surprisingly few methods for explicitly maximizing punctuality by optimizing the timetable exists in the literature. We study how late-stage adjustments during the capacity allocation can improve punctuality of the traffic. In this paper, we therefore extend a combined simulation-optimization method so it can be used to explicitly maximize the predicted punctuality of a given nonperiodic timetable on a double-track line. The method is evaluated in two microsimulation experiments in the southbound direction of the Swedish Western Main Line using Railsys. We compare the method in simulation with our previous method for minimizing total disutility, two methods from the scientific literature (light robustness, and robustness in critical points) and two naïve strategies. The methods’ effectiveness is assessed in a detailed statistical analysis considering end-station punctuality, total punctuality, and the robustness measure total disutility. Only light robustness results in timetables that in simulation performs better or equally well as the given timetable (based on the national timetable) with respect to all performance measures and evaluated scenarios. The method for maximizing punctuality performs best with respect to total punctuality., QC 20230927

Increasing the modal share of the single wagonload transport in Europe requires improving the reliability and predictability of freight trains running between the yards. In this paper, we propose a novel machine learning-assisted macro simulation framework to increase the predictability of yard departures and arrivals. Machine learning is applied through a random forest algorithm to implement a yard departure prediction model. Our yard departure prediction approach is less complex compared to previous yard simulation approaches, and provides an accuracy level of 92% in predictions. Then, departure predictions assist a macro simulation network model (PROTON) to predict arrivals to the succeeding yards. We tested this framework using data from a stretch between two main yards in Sweden; our experiments show that the current framework performs better than the timetable and a basic machine learning arrival prediction model by R2 of 0.48 and a mean absolute error of 35 minutes. Our current results indicate that combination of approaches, including yard and network interactions, can yield competitive results for complex yard arrival time prediction tasks which can assist yard operators and infrastructure managers in yard re-planning processes and yard-network coordination respectively., QC 20231122

Increasing the modal share of rail freight is an ongoing goal in Europe and North America. Yards can play an important role in realizing this target by their reliable and predictable performance. We aim at predicting yard departures by implementing a simulation-assisted machine learning model via two general and step-wise concepts for including the predictors. The former adds all predictors at once, and the latter adds them per the availability or the sub-yard. The data used for training the model is a one-year real-world operational data set from a European hump yard and multiple two-year simulation data sets from a representative hump yard in North America. To the best of our knowledge, no previous research has attempted to implement a generalizable prediction model between the European and the North American contexts. The model is developed on a decision tree algorithm based on a 10-fold cross-validation process. Comparing the model performance on three data sets: the real-world, a baseline simulation, and an ultimate randomness simulation shows that the model has a similar performance in the first two data sets with a respective R-squared of 0.90 and 0.87, which shows high capturing of the variance in the data. However, adding large randomness in the simulation decreases the R-squared to 0.70. Results for the step-wise inclusion of the predictors are different for the real-world and simulation data. For the former, adding more operational predictors does not change the model performance, whereas for the latter, adding departure yard predictors increases the R-squared substantially. The global feature importance shows that for the real-world data almost all predictors contribute to a great extent to the predictions, with maximum planned length, departure week day, and the number of arriving trains as the most contributing ones, whereas for the simulation data, the departure yard predictors provide the largest contribution., QC 20230517, FR8RAIL III, PRATA, Shift2Rail

Autonomous machines are expected to be vastly used at construction sites as they can efficiently perform repetitive and dangerous tasks. However, ensuring the operational safety of such autonomous machines in a highly dynamic environment is challenging. Although autonomous machines usually are equipped with a perception system that permits them to navigate locally, there is a need to share a global view of the construction site to reduce the risk of accidents or errors. A digital twin of the construction site map has the potential of fusing the real-time perception from different sources at the site, such as different autonomous machines working at the construction site, analysing them and sharing the needed information to operate safely and effectively at the site. This paper proposes the adoption of the recently published standard, ISO 23247 digital twin framework for manufacturing, to implement and maintain a dynamic map of construction sites. The proposed framework will enable safe and efficient operation of autonomous machines on construction sites.

[Context and Motivation] Requirements in tender documents are often mixed with other supporting information. Identifying requirements in large tender documents could aid the bidding process and help estimate the risk associated with the project. [Question/problem] Manual identification of requirements in large documents is a resource-intensive activity that is prone to human error and limits scalability. This study compares various state-of-the-art approaches for requirements identification in an industrial context. For generalizability, we also present an evaluation on a real-world public dataset. [Principal ideas/results] We formulate the requirement identification problem as a binary text classification problem. Various state-of-the-art classifiers based on traditional machine learning, deep learning, and few-shot learning are evaluated for requirements identification based on accuracy, precision, recall, and F1 score. Results from the evaluation show that the transformer-based BERT classifier performs the best, with an average F1 score of 0.82 and 0.87 on industrial and public datasets, respectively. Our results also confirm that few-shot classifiers can achieve comparable results with an average F1 score of 0.76 on significantly lower samples, i.e., only 20% of the data. [Contribution] There is little empirical evidence on the use of large language models and few-shots classifiers for requirements identification. This paper fills this gap by presenting an industrial empirical evaluation of the state-of-the-art approaches for requirements identification in large tender documents. We also provide a running tool and a replication package for further experimentation to support future research in this area.

In Sweden and other countries it is not an uncommon practice that freight trains depart more or less on-demand instead of strictly following a pre-planned timetable. However, the systematic effects of freight trains departing late or (in particular) early has long been a contested issue. Although some microscopic simulation tools currently have the capability to evaluate the effect of freight trains departing before schedule, it has yet not been established how macroscopic simulation tools, capable of fast simulation of nation-wide networks, can manage such tasks. This paper uses a case study on a line between two large freight yards in Sweden to investigate how the results of microscopic and macroscopic simulation, represented by two modern simulation tools, differ when it comes to this particular problem. The main findings are that both the microscopic and the macroscopic tools replicated the empirical punctuality fairly well. Furthermore, allowing early departures of freight trains increased overall freight train punctuality while the passenger train punctuality decreased slightly, as determined by both tools. The results are encouraging, but further studies are needed to determine if macroscopic simulation is on-par with microscopic simulation., QC 20220328

Construction sites are a special kind of off-road environment that needs dedicated dynamic maps to enable autonomous navigation in such terrains. In this paper, challenges for autonomous navigation on construction sites are first identified. Later, requirements for dynamic maps for autonomous navigation on construction sites are proposed based on the identified challenges., TRUST-SOS, IndTech

Historically, cylinder locks’ quality has been tested manually by human operators after full assembly. The frequency and the characteristics of the testing procedure for these locks wear the operators’ wrists and lead to varying results of the quality control. The consistency in the quality control is an important factor for the expected lifetime of the locks which is why the industry seeks an automated solution. This study evaluates how consistently the operators can classify a collection of locks, using their tactile sense, compared to a more objective approach, using torque measurements and Machine Learning (ML). These locks were deliberately chosen because they are prone to get inconsistent classifications, which means that there is no ground truth of how to classify them. The ML algorithms were therefore evaluated with two different labeling approaches, one based on the results from the operators, using their tactile sense to classify into ‘working’ or ‘faulty’ locks, and a second approach by letting an unsupervised learner create two clusters of the data which were then labeled by an expert using visual inspection of the torque diagrams. The results show that an ML-solution, trained with the second approach, can classify mechanical anomalies, based on torque data, more consistently compared to operators, using their tactile sense. These findings are a crucial milestone for the further development of a fully automated test procedure that has the potential to increase the reliability of the quality control and remove an injury-prone task from the operators.

Requirements engineering is a time-consuming process, and it can benefit significantly from automated tool support. Ambiguity detection in natural language requirements is a challenging problem in the requirements engineering community. Several Natural Language Processing tools and techniques have been developed to improve and solve the problem of ambiguity detection in natural language requirements. However, there is a lack of empirical evaluation of these tools. We aim to contribute the understanding of the empirical performance of such solutions by evaluating four tools using the dataset of 180 system requirements from the electric train propulsion system provided to us by our industrial partner Alstom. The tools that were selected for this study are Automated Requirements Measurement (ARM), Quality Analyzer for Requirement Specifications (QuARS), REquirements Template Analyzer (RETA), and Requirements Complexity Measurement (RCM). Our analysis showed that selected tools could achieve high recall. Two of them had the recall of 0.85 and 0.98. But they struggled to achieve high precision. The RCM, an in-house developed tool by our industrial partner Alstom, achieved the highest precision in our study of 0.68.

Test automation brings the potential to reduce costs and human effort, but several aspects of software testing remain challenging to automate. One such example is automated performance testing to find performance breaking points. Current approaches to tackle automated generation of performance test cases mainly involve using source code or system model analysis or use-case based techniques. However, source code and system models might not always be available at testing time. On the other hand, if the optimal performance testing policy for the intended objective in a testing process instead could be learnt by the testing system, then test automation without advanced performance models could be possible. Furthermore, the learnt policy could later be reused for similar software systems under test, thus leading to higher test efficiency. We propose SaFReL, a self-adaptive fuzzy reinforcement learning-based performance testing framework. SaFReL learns the optimal policy to generate performance test cases through an initial learning phase, then reuses it during a transfer learning phase, while keeping the learning running and updating the policy in the long term. Through multiple experiments on a simulated environment, we demonstrate that our approach generates the target performance test cases for different programs more efficiently than a typical testing process, and performs adaptively without access to source code and performance models.

Departure deviations from shunting yards impact the reliability of rail freight services and the punctuality of a railway network. Therefore, the statistical analysis of these deviations are necessary for improving the operation of trains in mixed-traffic networks. In our paper, we conduct a detailed statistical analysis of departure deviations considering individual shunting yards characteristics. We use a large freight train delay dataset comprising 250,000 departures over seven years for the two largest shunting yards in Sweden, comparable to other medium-sized shunting yards in Europe. To find the probability distribution of departure deviations, we compare four distribution functions including the exponential, the log-normal, the gamma, and the Weibull according to the maximum likelihood estimates and results of the Anderson-Darling goodness of fit test. In our experiments, we show that the log-normal distribution fits best for delayed departures across both shunting yards, and for early departures at one of them, whereas the gamma distribution fits best for early departures at the other yard. For the temporal delay distribution, we find that fluctuations in the network usage impact the percentage of delayed departures across hours and weekdays, but not across months or years. In addition, we find that freight trains are mostly delayed in the winter. In the case of hourly delayed departures, we demonstrate that a shunting yard involved with domestic traffic showed a negative correlation between delayed departures and the network usage, whereas an international shunting yard did not, which indicates individuality in shunting yard operations impact shunting yard-network interactions. Our findings mainly contribute to better understanding of departure deviations from shunting yards, thus enhancing the operations and capacity utilization of shunting yards. Moreover, delay distributions can be beneficial in handling delays in traffic management models as well as en, QC 20210331, Shift2Rail, FR8HUB, FR8RAIL III

In this paper we propose a macroscopic model framework for departure delay prediction from railyards. The railyard is a large area comprising three sub-yards (arrival, classification, departure). In fact, timely operation at railyard is dependent on coordinated operations in these sub-yards. More importantly, punctual functioning of railyards is crucial for increasing competitiveness of rail freight services throughout the network. Despite previous models, we considered railyard congestion at the arrival yard, time availability of each wagon at the classification yard, and time availability of locomotive at the departure yard. The core part of this paper analyzes the effect of congestion at arrival yard on departure delays. Punctuality data from two Swedish railyards for a seven-year period is used. The congestion is defined as the number of arriving trains three hours before each departure. The results showed that the highest number of delayed departures occur at congestion levels of 4-10, while correlation coefficient is around zero. Analysing the whole dataset reveals that these congestion levels are common for all departures not just the delayed ones. Therefore, we conclude that as three sub-yards are interrelated, a comprehensive definition of congestion at railyard level is required. An elaborate definition of congestion can make it a proper predictor for further delay prediction models., QC 20201130, Shift2Rail, FR8HUB

A quarry site is used to produce gravel with different granularities based on the customer's needs. The production process consists of different stages that need to be planned in order to achieve high productivity and low cost. During the operation of the quarry site, the operating parameters change, and the site productivity could be affected, therefore, a replanning of the production is needed. This paper presents a real-time optimization model for production planning in a quarry site. The optimization model consists of three modules, a productivity monitoring module, a simulation model, and an optimization algorithm. The productivity monitoring module produces the site productivity based on the current operating parameters of the quarry site. Once the site productivity reaches a specific threshold, the optimization algorithm is run on an updated simulation model to find the optimal values of the site parameters. The proposed real-time optimization model is applied to a real quarry site simulated on a high-fidelity simulator. The case study shows that the proposed optimization model can effectively improve the productivity of the quarry site and hence reduces the wastes and improve environmental sustainability.

The main objective of this paper is to develop a macroscopic delay estimation model for freight trains departing from the marshalling yard. Freight trains are made up in large marshalling yards comprising three yards (arrival, classification, departure). On time operations in marshaling yards enhances reliability of rail freight services compared to other modes of freight transport. Currently, freight trains encounter most of their delays in marshalling yards even before entering the railway network. Therefore, it is needed to estimate the departure delay of freight trains from the marshaling yard. So far, studies have mainly focused on classification yard operations to estimate departure delay, whereas a proper delay estimation model should be able to consider processes of all three yards. We have developed our model considering main factors (yard congestion, railcar availability and locomotive availability) from all three yards. Hallsberg and Malmö Marshalling yards in Sweden were used as case study., Part of proceedings: ISBN 978-952-311-484-5QC 20201124, Shift2Rail, FR8HUB

QC 20201026

Railway capacity planning aims to determine the amount of traffic that can be operated on a given infrastructure. The timetable compression method described in UIC Code 406 has become one of the standard tools in this area. Motivated by the Swedish Transportation Administration's timetable independent adaptation of the methodology and its need for extension we explore how the compression method can be applied to evaluate the capacity of the underlying infrastructure for strategic planning rather than the occupation ratio of a specific timetable. By performing ensemble averaging of scheduled train sequences we abstract from a single timetable concept and perform a distributional analysis of timetable utilization. To mitigate decomposition-induced underestimation of network effects the compression area is extended and approaches to include interdependencies between stations and lines are investigated. The methodology is applied for capacity assessment of railway stations and line segments in a case study based on the Swedish Southern Main Line rail corridor., QC 20221114

Performance testing involving performance test case generation and execution remains a challenge, particularly for complex systems. Different application-, platform- and workload-based factors can influence the performance of the software under test. Common approaches for generating the platform-based and workload-based test conditions are often based on system model or source code analysis, real usage modelling and use-case based design techniques. Nonetheless, those artifacts might not be always available during the testing. Moreover, creating a detailed performance model is often difficult. On the other hand, test automation solutions such as automated test case generation can enable effort and cost reduction with the potential to improve the intended test criteria coverage. Furthermore, if the optimal way (policy) to generate the test cases can be learnt by the testing system, then the learnt policy can be reused in further testing situations such as testing variants or evolved versions of the software, and upon changeable factors of testing process. This capability can lead to additional cost and computation time saving in the testing process. In this research, we have developed an autonomous performance testing framework using model-free reinforcement learning augmented by fuzzy logic and self-adaptive strategies. It is able to learn the optimal policy to generate different platform-based and workload-based test conditions without access to the system model and source code. The use of fuzzy logic and self-adaptive strategy helps to tackle the issue of uncertainty and improve the accuracy and adaptivity of the proposed learning. Our evaluation experiments showed that the proposed autonomous performance testing framework is able to generate the test conditions efficiently and in a way adaptive to varying testing situations., TESTOMAT Project - The Next Level of Test Automation, XIVT - eXcellence in Variant Testing

QCR 20210127

Minimal travel time and maximal reliability are two of the most important properties of a railway transportation service. This paper considers the problem of finding a timetable for a given set of departures that minimizes the weighted sum of scheduled travel time and expected delay, thereby capturing these two important socio-economic properties of a timetable. To accurately represent the complex secondary delays in operational railway traffic, an approach combining microscopic simulation and macroscopic timetable optimization is proposed. To predict the expected delay in the macroscopic timetable, a surrogate function is formulated, as well as a subproblem to calibrate the parameters in the model. In a set of computational experiments, the approach increased the socio-economic benefit by 2-5% and improved the punctuality by 8-25%., QC 20190924

The main goal of software testing is to detect as many hidden bugs as possible in the final software product before release. Generally, a software product is tested by executing a set of test cases, which can be performed manually or automatically. The number of test cases which are required to test a software product depends on several parameters such as the product type, size, and complexity. Executing all test cases with no particular order can lead to waste of time and resources. Test optimization can provide a partial solution for saving time and resources which can lead to the final software product being released earlier. In this regard, test case selection, prioritization, and scheduling can be considered as possible solutions for test optimization. Most of the companies do not provide direct support for ranking test cases on their own servers. In this paper, we introduce, apply, and evaluate sOrTES as our decision support system for manual integration of test scheduling. sOrTES is a Python-based supportive tool which schedules manual integration test cases which are written in a natural language text. The feasibility of sOrTES is studied by an empirical evaluation which has been performed on a railway use-case at Bombardier Transportation, Sweden. The empirical evaluation indicates that around 40% of testing failure can be avoided by using the proposed execution schedules by sOrTES, which leads to an increase in the requirements coverage of up to 9.6%., QC 20190404

Satisfying performance requirements is of great importance for performance-critical software systems. Performance analysis to provide an estimation of performance indices and ascertain whether the requirements are met is essential for achieving this target. Model-based analysis as a common approach might provide useful information but inferring a precise performance model is challenging, especially for complex systems. Performance testing is considered as a dynamic approach for doing performance analysis. In this work-in-progress paper, we propose a self-adaptive learning-based test framework which learns how to apply stress testing as one aspect of performance testing on various software systems to find the performance breaking point. It learns the optimal policy of generating stress test cases for different types of software systems, then replays the learned policy to generate the test cases with less required effort. Our study indicates that the proposed learning-based framework could be applied to different types of software systems and guides towards autonomous performance testing.

Knowing about dependencies and similarities between test cases is beneficial for prioritizing them for cost-effective test execution. This holds especially true for the time consuming, manual execution of integration test cases written in natural language. Test case dependencies are typically derived from requirements and design artifacts. However, such artifacts are not always available, and the derivation process can be very time-consuming. In this paper, we propose, apply and evaluate a novel approach that derives test cases' similarities and functional dependencies directly from the test specification documents written in natural language, without requiring any other data source. Our approach uses an implementation of Doc2Vec algorithm to detect text-semantic similarities between test cases and then groups them using two clustering algorithms HDBSCAN and FCM. The correlation between test case text-semantic similarities and their functional dependencies is evaluated in the context of an on-board train control system from Bombardier Transportation AB in Sweden. For this system, the dependencies between the test cases were previously derived and are compared to the results our approach. The results show that of the two evaluated clustering algorithms, HDBSCAN has better performance than FCM or a dummy classifier. The classification methods' results are of reasonable quality and especially useful from an industrial point of view. Finally, performing a random undersampling approach to correct the imbalanced data distribution results in an F1 Score of up to 75% when applying the HDBSCAN clustering algorithm.