Digitala Vetenskapliga Arkivet

Change search
Refine search result
1 - 7 of 7
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Rows per page
  • 5
  • 10
  • 20
  • 50
  • 100
  • 250
Sort
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
  • Standard (Relevance)
  • Author A-Ö
  • Author Ö-A
  • Title A-Ö
  • Title Ö-A
  • Publication type A-Ö
  • Publication type Ö-A
  • Issued (Oldest first)
  • Issued (Newest first)
  • Created (Oldest first)
  • Created (Newest first)
  • Last updated (Oldest first)
  • Last updated (Newest first)
  • Disputation date (earliest first)
  • Disputation date (latest first)
Select
The maximal number of hits you can export is 250. When you want to export more records please use the Create feeds function.
  • 1.
    Helali Moghadam, Mahshid
    et al.
    RISE Research Institutes of Sweden, Digital Systems, Industrial Systems.
    Hamidi, Golrokh
    Mälardalen University, Sweden.
    Borg, Markus
    RISE Research Institutes of Sweden, Digital Systems, Mobility and Systems.
    Saadatmand, Mehrdad
    RISE Research Institutes of Sweden, Digital Systems, Industrial Systems.
    Bohlin, Markus
    Mälardalen University, Sweden.
    Lisper, Björn
    Mälardalen University, Sweden.
    Potena, Pasqualina
    RISE Research Institutes of Sweden, Digital Systems, Industrial Systems.
    Performance Testing Using a Smart Reinforcement Learning-Driven Test Agent2021In: 2021 IEEE Congress on Evolutionary Computation (CEC), 2021, p. 2385-2394Conference paper (Refereed)
    Abstract [en]

    Performance testing with the aim of generating an efficient and effective workload to identify performance issues is challenging. Many of the automated approaches mainly rely on analyzing system models, source code, or extracting the usage pattern of the system during the execution. However, such information and artifacts are not always available. Moreover, all the transactions within a generated workload do not impact the performance of the system the same way, a finely tuned workload could accomplish the test objective in an efficient way. Model-free reinforcement learning is widely used for finding the optimal behavior to accomplish an objective in many decision-making problems without relying on a model of the system. This paper proposes that if the optimal policy (way) for generating test workload to meet a test objective can be learned by a test agent, then efficient test automation would be possible without relying on system models or source code. We present a self-adaptive reinforcement learning-driven load testing agent, RELOAD, that learns the optimal policy for test workload generation and generates an effective workload efficiently to meet the test objective. Once the agent learns the optimal policy, it can reuse the learned policy in subsequent testing activities. Our experiments show that the proposed intelligent load test agent can accomplish the test objective with lower test cost compared to common load testing procedures, and results in higher test efficiency.

  • 2.
    Demeyer, Serge
    et al.
    Universiteit Antwerpen Flanders Make vzw, Belgium.
    Causevic, Adnan
    Mälardalen University, Sweden.
    Wiklund, Kristian
    Ericsson AB, Sweden.
    Potena, Pasqualina
    RISE Research Institutes of Sweden, Digital Systems, Industrial Systems.
    The Next Level of Test Automation (NEXTA 2020)2020In: 2020 IEEE International Conference on Software Testing, Verification and Validation Workshops (ICSTW), 2020, p. xxii-xxiiConference paper (Refereed)
    Abstract [en]

    Test automation has been an acknowledged software engineering best practice for years. However, the topic involves more than the repeated execution of test cases that often comes first to mind. Simply running test cases using a unit testing framework is no longer enough for test automation to keep up with the ever-shorter release cycles driven by continuous deployment and technological innovations such as microservices and DevOps pipelines. Now test automation needs to rise to the next level by going beyond mere test execution.

  • 3.
    Gonzalez-Hernandez, Loreto
    et al.
    University of Skövde, Sweden.
    Lindström, Birgitta
    University of Skövde, Sweden.
    Offutt, Jeff
    George Mason University, USA.
    Andler, Sten F.
    University of Skövde, Sweden.
    Potena, Pasqualina
    RISE - Research Institutes of Sweden (2017-2019), SICS.
    Bohlin, Markus
    RISE - Research Institutes of Sweden (2017-2019), SICS, Sweden.
    Using Mutant Stubbornness to Create Minimal and Prioritized Test Sets2018In: 2018 IEEE International Conference on Software Quality, Reliability and Security,  QRS 2018, 2018, p. 446-457Conference paper (Refereed)
    Abstract [en]

    In testing, engineers want to run the most useful tests early (prioritization). When tests are run hundreds or thousands of times, minimizing a test set can result in significant savings (minimization). This paper proposes a new analysis technique to address both the minimal test set and the test case prioritization problems. This paper precisely defines the concept of mutant stubbornness, which is the basis for our analysis technique. We empirically compare our technique with other test case minimization and prioritization techniques in terms of the size of the minimized test sets and how quickly mutants are killed. We used seven C language subjects from the Siemens Repository, specifically the test sets and the killing matrices from a previous study. We used 30 different orders for each set and ran every technique 100 times over each set. Results show that our analysis technique performed significantly better than prior techniques for creating minimal test sets and was able to establish new bounds for all cases. Also, our analysis technique killed mutants as fast or faster than prior techniques. These results indicate that our mutant stubbornness technique constructs test sets that are both minimal in size, and prioritized effectively, as well or better than other techniques.

  • 4.
    Flemström, Daniel
    et al.
    Mälardalen University, Sweden.
    Potena, Pasqualina
    RISE - Research Institutes of Sweden, ICT, SICS.
    Sundmark, Daniel
    Mälardalen University, Sweden.
    Afzal, Wasif
    Mälardalen University, Sweden.
    Bohlin, Markus
    RISE - Research Institutes of Sweden, ICT, SICS.
    Similarity-based prioritization of test case automation2018In: Software quality journal, ISSN 0963-9314, E-ISSN 1573-1367, Vol. 26, no 4, p. 1421-1449Article in journal (Refereed)
    Abstract [en]

    The importance of efficient software testing procedures is driven by an ever increasing system complexity as well as global competition. In the particular case of manual test cases at the system integration level, where thousands of test cases may be executed before release, time must be well spent in order to test the system as completely and as efficiently as possible. Automating a subset of the manual test cases, i.e, translating the manual instructions to automatically executable code, is one way of decreasing the test effort. It is further common that test cases exhibit similarities, which can be exploited through reuse when automating a test suite. In this paper, we investigate the potential for reducing test effort by ordering the test cases before such automation, given that we can reuse already automated parts of test cases. In our analysis, we investigate several approaches for prioritization in a case study at a large Swedish vehicular manufacturer. The study analyzes the effects with respect to test effort, on four projects with a total of 3919 integration test cases constituting 35,180 test steps, written in natural language. The results show that for the four projects considered, the difference in expected manual effort between the best and the worst order found is on average 12 percentage points. The results also show that our proposed prioritization method is nearly as good as more resource demanding meta-heuristic approaches at a fraction of the computational time. Based on our results, we conclude that the order of automation is important when the set of test cases contain similar steps (instructions) that cannot be removed, but are possible to reuse. More precisely, the order is important with respect to how quickly the manual test execution effort decreases for a set of test cases that are being automated. 

    Download full text (pdf)
    fulltext
  • 5.
    Pietrantuono, Roberto
    et al.
    CINI National Interuniversity Consortium for Informatics, Italy.
    Potena, Pasqualina
    RISE - Research Institutes of Sweden, ICT, SICS.
    Pecchia, Antonio
    CINI National Interuniversity Consortium for Informatics, Italy.
    Rodriguez, Daniel
    University of Alcala, Spain.
    Russo, Stefano
    Federico II University of Naples, Italy.
    Fernandez, Luis
    University of Alcala, Spain.
    Multi-Objective Testing Resource Allocation under Uncertainty2018In: IEEE Transactions on Evolutionary Computation, ISSN 1089-778X, E-ISSN 1941-0026, Vol. 22, no 3, p. 347-362Article in journal (Refereed)
    Abstract [en]

    Testing resource allocation is the problem of planning the assignment of resources to testing activities of software components so as to achieve a target goal under given constraints. Existing methods build on Software Reliability Growth Models (SRGMs), aiming at maximizing reliability given time/cost constraints, or at minimizing cost given quality/time constraints. We formulate it as a multi-objective debug-aware and robust optimization problem under uncertainty of data, advancing the stateof- the-art in the following ways. Multi-objective optimization produces a set of solutions, allowing to evaluate alternative tradeoffs among reliability, cost and release time. Debug awareness relaxes the traditional assumptions of SRGMs – in particular the very unrealistic immediate repair of detected faults – and incorporates the bug assignment activity. Robustness provides solutions valid in spite of a degree of uncertainty on input parameters. We show results with a real-world case study.

  • 6.
    Gonzalez-Hernandez, Loreto
    et al.
    University of Skövde, Sweden.
    Lindström, Birgitta
    University of Skövde, Sweden.
    Offutt, Jeff
    George Mason University, USA.
    Andler, Sten F.
    University of Skövde, Sweden.
    Potena, Pasqualina
    RISE - Research Institutes of Sweden, ICT, SICS.
    Bohlin, Markus
    RISE - Research Institutes of Sweden.
    Using Mutant Stubbornness to Create Minimal and Prioritized Test Sets2018In: 2018 IEEE International Conference on Software Quality, Reliability and Security,  QRS 2018, 2018, p. 446-457Conference paper (Refereed)
    Abstract [en]

    In testing, engineers want to run the most useful tests early (prioritization). When tests are run hundreds or thousands of times, minimizing a test set can result in significant savings (minimization). This paper proposes a new analysis technique to address both the minimal test set and the test case prioritization problems. This paper precisely defines the concept of mutant stubbornness, which is the basis for our analysis technique. We empirically compare our technique with other test case minimization and prioritization techniques in terms of the size of the minimized test sets and how quickly mutants are killed. We used seven C language subjects from the Siemens Repository, specifically the test sets and the killing matrices from a previous study. We used 30 different orders for each set and ran every technique 100 times over each set. Results show that our analysis technique performed significantly better than prior techniques for creating minimal test sets and was able to establish new bounds for all cases. Also, our analysis technique killed mutants as fast or faster than prior techniques. These results indicate that our mutant stubbornness technique constructs test sets that are both minimal in size, and prioritized effectively, as well or better than other techniques.

  • 7.
    Lisper, Björn
    et al.
    Mälardalen University, Sweden.
    Lindstrom, Birgitta
    University of Skövde, Sweden.
    Potena, Pasqualina
    RISE - Research Institutes of Sweden, ICT, SICS.
    Saadatmand, Mehrdad
    RISE - Research Institutes of Sweden, ICT, SICS.
    Bohlin, Markus
    RISE - Research Institutes of Sweden, ICT, SICS.
    Targeted Mutation: Efficient Mutation Analysis for Testing Non-Functional Properties2017In: Proceedings - 10th IEEE International Conference on Software Testing, Verification and Validation Workshops, ICSTW 2017, 2017, p. 65-68Conference paper (Refereed)
    Abstract [en]

    Mutation analysis has proven to be a strong technique for software testing. Unfortunately, it is also computationally expensive and researchers have therefore proposed several different approaches to reduce the effort. None of these reduction techniques however, focuses on non-functional properties. Given that our goal is to create a strong test suite for testing a certain non-functional property, which mutants should be used? In this paper, we introduce the concept of targeted mutation, which focuses mutation effort to those parts of the code where a change can make a difference with respect to the targeted non-functional property. We show how targeted mutation can be applied to derive efficient test suites for estimating the Worst-Case Execution Time (WCET). We use program slicing to direct the mutations to the parts of the code that are likely to have the strongest influence on execution time. Finally, we outline an experimental procedure for how to evaluate the technique.

1 - 7 of 7
CiteExportLink to result list
Permanent link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf