Swedish Institute of Computer Science Publications Database
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Cost-Benefit Analysis of Using Dependency Knowledge at Integration Testing
No full textIn software system development, testing can take considerable time and resources, and there are numerous examples in the literature of how to improve the testing process. In particular, methods for selection and prioritization of test cases can play a critical role in efficient use of testing resources. This paper focuses on the problem of selection and ordering of integration-level test cases. Integration testing is performed to evaluate the correctness of several units in composition. Further, for reasons of both effectiveness and safety, many embedded systems are still tested manually. To this end, we propose a process, supported by an online decision support system, for ordering and selection of test cases based on the test result of previously executed test cases. To analyze the economic efficiency of such a system, a customized return on investment (ROI) metric tailored for system integration testing is introduced. Using data collected from the development process of a large-scale safety-critical embedded system, we perform Monte Carlo simulations to evaluate the expected ROI of three variants of the proposed new process. The results show that our proposed decision support system is beneficial in terms of ROI at system integration testing and thus qualifies as an important element in improving the integration testing process
A DECISION SUPPORT SYSTEM FOR INTEGRATION TEST SELECTION
No full textSoftware testing generally suffers from time and budget limitations. Indiscriminately
executing all available test cases leads to sub-optimal exploitation
of testing resources. Selecting too few test cases for execution on the other
hand might leave a large number of faults undiscovered. Test case selection and
prioritization techniques can lead to more efficient usage of testing resources
and also early detection of faults. Test case selection addresses the problem of
selecting a subset of an existing set of test cases, typically by discarding test
cases that do not improve the quality of the system under test. Test case prioritization
schedules test cases for execution in order to increase their effectiveness
at achieving some performance goals such as: earlier fault detection, optimal
allocation of testing resources and reducing overall testing effort. In practice,
prioritized selection of test cases requires the evaluation of different test case
criteria. Therefore this problem can be formulated as a multi-criteria decision
making problem. As the number of decision criteria grows, application of a
systematic decision making solution becomes a necessity. In this thesis, we
propose a tool-supported framework using a decision support system, for prioritizing
and selecting integration test cases in embedded system development.
This framework provides a complete loop for selecting the best candidate test
case for execution based on a finite set of criteria. The results of multiple
case studies, done on a train control management subsystem, from Bombardier
Transportation AB in Sweden, demonstrate how our approach helps to select
test cases in a systematic way. This can lead to early detection of faults while
respecting various criteria. Also, we have evaluated a customized return on
investment metric to quantify the economic benefits in optimizing system integration
testing using our framework
Demo: Experimental Feasibility Study of CCN-lite on Contiki Motes for IoT Data Streams
No full textMany IoT applications are inherently information-centric, making it advantageous to use ICN transport. We demonstrate CCN-lite ported to run on Contiki sensor motes with limited processing and storage resources. We show a method for mapping streams of sensor data to a stream of immutable CCN named data objects, and an adaptive probing method to find the newest value. We also demonstrate interoperation between MQTT and CCN via a gateway. A higher level goal is to use ICN as an open interface for accessing IoT data
Systematic Derivation of Bounds and Glue Constraints for Time-Series Constraints
No full textInteger time series are often subject to constraints on the aggregation of the integer features of all occurrences of some pattern within the series. For example, the number of inflexions may be constrained, or the sum of the peak maxima, or the minimum of the peak widths. It is currently unknown how to maintain domain consistency efficiently on such constraints. We propose parametric ways of systematically deriving glue constraints, which are a particular kind of implied constraints, as well as aggregation bounds that can be added to the decomposition of time-series constraints. We evaluate the beneficial propagation impact of the derived implied constraints and bounds, both alone and together
S3K: Scalable Security With Symmetric Keys—DTLS Key Establishment for the Internet of Things
No full textDTLS is becoming the de facto standard for communication security in the Internet of Things (IoT). In order to run the DTLS protocol, one needs to establish keys between the communicating devices. The default method of key establishment requires X.509 certificates and a Public Key Infrastructure, an approach which is often too resource consuming for small IoT devices. DTLS also supports the use of preshared keys and raw public keys. These modes are more lightweight, but they are not scalable to a large number of devices
SADHealth: A Personal Mobile Sensing System for Seasonal Health Monitoring
No full textPeople’s health, mood and activities are closely related to their environment and the seasons. Countries at extreme latitudes (e.g. Sweden, UK and Norway) experience huge variations in their light levels, impacting the population’s mental state, well-being and energy levels. Advanced sensing technologies on smartphones enable non-intrusive and longitudinal monitoring of user states. The collected data makes it possible for healthcare professionals and individuals to diagnose and rectify problems caused by seasonality.
In this paper, we present a personal mobile sensing system that exploits technologies on smartphones to efficiently and accurately detect the light exposure, mood and activity levels of individuals. We conducted a two year experiment with many users to test the functionality and performance of our system. The results show that we can obtain accurate light exposure estimation by opportunistically measuring light data on smartphones, tracking both personal light exposure and the general seasonal trends. An optional questionnaire also allows insight into the correlation between a user’s mood and energy levels. Consequently, we were able quantitatively inform users with winter blues how little light they were experiencing and also correlate this with their reduced mood and energy, providing evidence for lifestyle changes
Testing Quality Requirements of a System-of-Systems in the Public Sector - Challenges and Potential Remedies
No full textQuality requirements is a difficult concept in software projects, and testing software qualities is a well-known challenge. Without proper management of quality requirements, there is an increased risk that the software product un-der development will not meet the expectations of its future users. In this pa-per, we share experiences from testing quality requirements when developing a large system-of-systems in the public sector in Sweden. We complement the experience reporting by analyzing documents from the case under study. As a final step, we match the identified challenges with solution proposals from the literature. We report five main challenges covering inadequate re-quirements engineering and disconnected test managers. Finally, we match the challenges to solutions proposed in the scientific literature, including in-tegrated requirements engineering, the twin peaks model, virtual plumblines, the QUPER model, and architecturally significant requirements. Our experi-ences are valuable to other large development projects struggling with testing of quality requirements. Furthermore, the report could be used by as input to process improvement activities in the case under study
Do Take it Personal: It's Not What You Say, It's Who (and Where) You Are!
No full textIssue management in market-driven software projects is constantly under time pressure. A limited set of developers must share their time between developing features for the next release and resolving reported issues. Project managers need to find the appropriate balance between a high quality product and fast time to market. We study a telecom company in Sweden developing embedded systems for a consumer market. The project managers report that developers resolve approximately 10% of the issues reported during a project. Consequently, it is critical to properly prioritize the issues to receive the best possible return on investment, and above all to remove all bugs that might impact the market's reception of the product. We use machine learning to investigate what features of an issue report are the best predictors of changes to production code during its corresponding resolution. After removing all features jeopardizing the confidentiality of individual engineers, the issue reports are characterized by 19 features (apart from text). We extract 80,000 issue reports, an equal mix of positive and negative examples, and train a Bayesian Network classifier [2], obtaining 73% classification accuracy. Moreover, it reveals that the feature with the highest predictive value is from which physical site the issue was submitted. The general priority feature however, is only ranked 17 out of 19, whereas the submitting team is ranked 12. Our findings confirm a suspicion in the company: the priority set by the issue submitter is indeed a poor predictor of a future code change