Monica Kristiansen Holone

• Waqas, Muhammad; Ali, Zohaib; Sánchez-Gordón, Mary & Holone, Monica Kristiansen (2024). Using LowCode and NoCode Tools in DevOps: A Multivocal Literature Review. In Mejia, Jezreel; Muñoz, Mirna; Alvaro, Rocha; Pérez, Yasmin Hernández & Avila-George, Himer (Ed.), New Perspectives in Software Engineering. Springer Nature. ISSN 9783031505898. p. 71–87. doi: 10.1007/978-3-031-50590-4_5. Full text in Research Archive
• Demi, Selina; Sánchez-Gordón, Mary; Holone, Monica Kristiansen & Larrucea, Xabier (2024). Trustworthy and collaborative traceability management: Experts’ feedback on a blockchain-enabled framework. Journal of Software: Evolution and Process (JSEP). ISSN 2047-7473. 36(11). doi: 10.1002/smr.2707. Full text in Research Archive Show summary

  Blockchain technology has attracted significant attention in both academia and industry. Recently, the application of blockchain has been advocated in software engineering. The global software engineering paradigm exacerbates trust issues, as distributed and cross-organizational teams need to share software artifacts. In such a context, there is a need for a decentralized yet reliable traceability knowledge base to keep track of what/how/when/by whom software artifacts were created or changed. This study presents a blockchain-enabled framework for trustworthy and collaborative traceability management and identifies benefits, challenges, and potential improvements based on the feedback of software engineering experts. A qualitative approach was followed in this study through semistructured interviews with software engineering (SE) experts. Transcripts were analyzed by applying the content analysis technique. The results indicated the emergence of five categories, further grouped into three main categories: experts' perceptions, blockchain-based software process improvement, and experts' recommendations. In addition, the findings suggested four archetypes of organizations that may be interested in blockchain technology: distributed organizations, organizations with contract-based projects, organizations in regulated domains, and regulators who may push the use of this technology. Further efforts should be devoted to the integration of the proposal with tools used throughout the software development lifecycle and leveraging the potential of smart contracts in validating the implementation of requirements automatically.

• Sánchez-Gordón, Mary; Colomo-Palacios, Ricardo; Akbar, Muhammad Azeem & Holone, Monica Kristiansen (2023). A Perspective on the Role of Human Behaviors in Software Development: Voice and Silence. arXiv.org. ISSN 2331-8422. doi: 10.48550/arXiv.2304.12903. Full text in Research Archive
• Demi, Selina; Sánchez-Gordón, Mary & Kristiansen, Monica Lind (2022). Blockchain for requirements traceability: A qualitative approach. Journal of Software: Evolution and Process (JSEP). ISSN 2047-7473. doi: 10.1002/smr.2493. Full text in Research Archive Show summary

  Blockchain technology has emerged as a “disruptive innovation” that has received significant attention in academic and organizational settings. However, most of the existing research is focused on technical issues of blockchain systems, overlooking the organizational perspective. This study adopted a grounded theory to unveil the blockchain implementation process in organizations from the lens of blockchain experts. The results revealed three main categories: key activities, success factors, and challenges related to blockchain implementation in organizations, the latter being identified as the core category, along with 17 other concepts. Findings suggested that the majority of blockchain projects stop at the pilot stage and outlined organizational resistance to change as the core challenge. According to the experts, the following factors contribute to the organizational resistance to change: innovation–production gap, conservative management, and centralized mentality. The study aims to contribute to the existing blockchain literature by providing a holistic and domain-agnostic view of the blockchain implementation process in organizational settings. This can potentially encourage the development and implementation of blockchain solutions and guide practitioners who are interested in leveraging the inherent benefits of this technology. In addition, the results are used to improve a blockchain-enabled requirements traceability framework proposed in our previous paper.

• Sandeep, R.C.; Sánchez-Gordón, Mary; Colomo-Palacios, Ricardo & Kristiansen, Monica Lind (2022). Effort Estimation in Agile Software Development: A Exploratory Study of Practitioners’ Perspective. In Przybyłek, Adam; Jarzębowicz, Aleksander; Luković, IVan & Ng, Yen Ying (Ed.), Lean and Agile Software Development: 6th international conference, LASD 2022, virtual event, January 22, 2022, proceedings. Springer. ISSN 9783030942373. p. 136–149. doi: 10.1007/978-3-030-94238-0_8. Full text in Research Archive
• Saheed, Yakub Kayode; Abiodun, Aremu Idris; Misra, Sanjay; Kristiansen, Monica Lind & Colomo-Palacios, Ricardo (2022). A machine learning-based intrusion detection for detecting internet of things network attacks. Alexandria Engineering Journal. ISSN 1110-0168. 61(12), p. 9395–9409. doi: 10.1016/j.aej.2022.02.063. Full text in Research Archive Show summary

  The Internet of Things (IoT) refers to the collection of all those devices that could connect to the Internet to collect and share data. The introduction of varied devices continues to grow tremendously, posing new privacy and security risks—the proliferation of Internet connections and the advent of new technologies such as the IoT. Various and sophisticated intrusions are driving the IoT paradigm into computer networks. Companies are increasing their investment in research to improve the detection of these attacks. By comparing the highest rates of accuracy, institutions are picking intelligent procedures for testing and verification. The adoption of IoT in the different sectors, including health, has also continued to increase in recent times. Where the IoT applications became well known for technology researchers and developers. Unfortunately, the striking challenge of IoT is the privacy and security issues resulting from the energy limitations and scalability of IoT devices. Therefore, how to improve the security and privacy challenges of IoT remains an important problem in the computer security field. This paper proposes a machine learning-based intrusion detection system (ML-IDS) for detecting IoT network attacks. The primary objective of this research focuses on applying ML-supervised algorithm-based IDS for IoT. In the first stage of this research methodology, feature scaling was done using the Minimum-maximum (min–max) concept of normalization on the UNSW-NB15 dataset to limit information leakage on the test data. This dataset is a mixture of contemporary attacks and normal activities of network traffic grouped into nine different attack types. In the next stage, dimensionality reduction was performed with Principal Component Analysis (PCA). Lastly, six proposed machine learning models were used for the analysis. The experimental results of our findings were evaluated in terms of validation dataset, accuracy, the area under the curve, recall, F1, precision, kappa, and Mathew correlation coefficient (MCC). The findings were also benchmarked with the existing works, and our results were competitive with an accuracy of 99.9% and MCC of 99.97%.

• Johansen, Stig Henning; Colomo-Palacios, Ricardo; Samuelsen, Terje & Kristiansen, Monica Lind (2019). Engaging students in the context of software project management teaching. In Conde, M. (Eds.), TEEM'19: Proceedings of the Seventh International Conference on Technological Ecosystems for Enhancing Multiculturality. Association for Computing Machinery (ACM). ISSN 9781450371919. p. 54–58. doi: 10.1145/3362789.3362795. Full text in Research Archive
• Samuelsen, Terje; Colomo-Palacios, Ricardo & Kristiansen, Monica Lind (2016). Learning software project management in teams with diverse backgrounds. In García-Peñalvo, F.J. (Eds.), Proceedings of the Fourth International Conference on Technological Ecosystems for Enhancing Multiculturality. Association for Computing Machinery (ACM). ISSN 9781450347471. p. 127–131. doi: 10.1145/3012430.3012507. Full text in Research Archive
• Due, Beathe; Kristiansen, Monica; Colomo-Palacios, Ricardo & Hien, Dang Ha The (2015). Introducing big data topics: a multicourse experience report from Norway. In Alves, G. (Eds.), Proceedings of the 3rd International Conference on Technological Ecosystems for Enhancing Multiculturality. ACM Publications. ISSN 9781450334426. p. 565–569. doi: 10.1145/2808580.2808667. Full text in Research Archive
• Kristiansen, Monica; Natvig, Bent & Winther, Rune (2014). Assessing software reliability of multistate systems. In Steenbergen, Raphaël; Gelder, P.H.A.J.M. van; Miraglia, S. & Vrouwenvelder, A.C.W.M. (Ed.), Safety, reliability and risk analysis : beyond the horizon : proceedings of the European Safety and Reliability Conference, ESREL 2013, Amsterdam, the Netherlands, 29 September-2 October 2013. CRC Press. ISSN 9781138001237. Full text in Research Archive
• Kristiansen, Monica; Nätt, Tom Heine & Heide, Christian F. (2013). Kvantitativ undersøkelse av mulige sammenhenger mellom vurderingsform og karakterer i høyere utdanning. Uniped - Tidsskrift for universitets- og høgskolepedagogikk. ISSN 1500-4538. 36(2), p. 62–80. doi: 10.3402/uniped.v36i2.21516. Full text in Research Archive Show summary

  I denne artikkelen undersøker vi om det gis bedre karakterer når vurderingsformen er mappe enn når vurderingsformen er tradisjonell skriftlig eksamen. For å gjøre dette, har vi tatt utgangspunkt i karakterene studenter ved Høgskolen i Østfold (HiØ) har fått i perioden f.o.m. høstsemesteret 2006 t.o.m. høstsemesteret 2010 i alle emner ved alle avdelinger. Resultatene fra undersøkelsene viser at studenter i gjennomsnitt får bedre karakterer dersom vurderingsformen er mappe enn når vurderingsformen er skriftlig eksamen. I våre data ligger gjennomsnittlig differanse mellom mappevurdering og skriftlig eksamen på 0,86 karaktertrinn når vi ser på alle avdelingene samlet. Størst gjennomsnittlig differanse finner vi ved avdeling for informasjonsteknologi. Ved denne avdelingen er gjennomsnittlig differanse mellom mappevurdering og skriftlig eksamen på hele 1,25 karaktertrinn. Videre viser våre data at omkring 80 % av studentene får bedre gjennomsnittskarakter på sine mappevurderinger enn på sine skriftlige eksamener når vi ser på alle avdelingene samlet. Ved avdeling for informasjonsteknologi får hele 88,7 % av studentene bedre gjennomsnittskarakter på sine mappevurderinger enn på sine skriftlige eksamener. Dette har imidlertid også sammenheng med studentenes gjennomsnittskarakter

• Nätt, Tom Heine; Heide, Christian F. & Kristiansen, Monica (2013). Myter og sannheter om årsaker til studenters prestasjoner i programmering. In Tøssebro, Erlend & Meling, Hein (Ed.), Norsk informatikkonferanse NIK 2013, Universitetet i Stavanger, 18. - 20.november 2013. Akademika forlag. ISSN 9788232103652. p. 76–87. Full text in Research Archive Show summary

  I denne artikkelen studerer vi studentenes karakterer gitt i et introduksjonsemne i programmering de åtte siste årene innenfor bachelorstudiene ingeniørfag - data, informatikk, informasjonssystemer, digital medieproduksjon og årsstudiet i informasjonsteknologi ved Høgskolen i Østfold. Spesielt undersøker vi hvorvidt studentenes karakterer i dette emnet korrelerer med karakterer i andre emner som også krever abstrakt tenkning (matematikk og objektorientert programmering), samt studentenes gjennomsnittskarakter i alle andre emner. I tillegg undersøker vi om mannlige studenter generelt presterer bedre enn kvinnelige studenter, og om dette i noen grad varierer mellom de ulike studieretningene. Resultatene fra undersøkelsene viser bl.a. at kvinnelige studenter gjør det 0.7 karaktertrinn dårligere enn sine mannlige medstudenter i introduksjonsemnet Innføring i programmering, men at dette langt ifra er signifikant for alle studieprogrammene. Videre viser resultatene korrelasjoner i størrelsesorden 0.5-0.6 mellom karakterene gitt i introduksjonsemnet og karakterene gitt i matematikk, objektorientert programmering og studentenes gjennomsnittskarakter.

• Heide, Christian F.; Kristiansen, Monica Lind & Nätt, Tom Heine (2012). Vurderingsform og karakterbruk. In Aalberg, Trond (Eds.), Norsk informatikkonferanse NIK 2012; Universitetet i Nordland 19 – 21 november 2012. Akademika forlag. ISSN 9788232100132. p. 61–71. Full text in Research Archive Show summary

  I denne artikkelen tar vi utgangspunkt i emner gitt ved ulike avdelinger ved Høgskolen i Østfold (HiØ), og undersøker om det gis bedre karakterer i emner hvor vurderingsformen er mappe enn emner hvor vurderingsformen er skriftlig eksamen. I tillegg undersøker vi i hvilken grad dette varierer mellom ulike avdelinger ved HiØ. Videre undersøker vi om svake karakterer brukes sjeldnere i emner som vurderes med mappe enn i emner som vurderes med skriftlig eksamen, og om dette i noen grad varierer mellom de ulike avdelingene. Som utgangspunkt for analysen benyttes karakterene studenter ved HiØ har f°att f.o.m. høstsemesteret 2004 t.o.m. v°arsemesteret 2012. Resultatene fra undersøkelsene viser at gjennomsnittskarakterene generelt er bedre i emner hvor vurderingsformen er mappe enn i emner hvor vurderingsformen er skriftlig eksamen. Datamaterialet viser at gjennomsnittskarakterene i emner vurdert med mappe ligger omkring 0.6-1.0 karaktertrinn høyere enn gjennomsnittskarakterene i emner vurdert med skriftlig eksamen. Videre viser resultatene at variansen i studentenes karakterer er signifikant større i emner med skriftlig eksamen enn i emner med mappevurdering. Data viser at standardavviket i emner med skriftlig eksamen er ca 0.26-0.46 karaktertrinn større enn standardavviket i emner med mappevurdering. Videre viser resultatene tydelig mindre hyppig bruk av svake karakterer i emner som vurderes med mappe enn i emner som vurderes med skriftlig eksamen.

• Kristiansen, Monica Lind; Winther, Rune & Natvig, Bent (2012). Establishing prior probability distributions for probabilities that pairs of software components fail simultaneously. In Bérenguer, Christophe; Grall, Antoine & Soares, Carlos Guedes (Ed.), Advances in Safety, Reliability and Risk Management - proceedings of the European Safety and Reliability Conference, ESREL 2011. CRC Press. ISSN 9780415683791. p. 96–104. doi: 10.1142/s021853931100410x. Full text in Research Archive
• Kristiansen, Monica Lind; Natvig, Bent & Winther, Rune (2012). A component-based approach for assessing reliability of compound software. In PSAM&ESREL, . (Eds.), 11th International Probabilistic Safety Assessment and Management Conference and the Annual European Safety and Reliability Conference 2012, 25-29 June 2012, Helsinki, Finland. Curran Associates, Inc.. ISSN 9781622764365. p. 1823–1832. Full text in Research Archive
• Kristiansen, Monica Lind; Winther, Rune & Natvig, Bent (2011). A Bayesian hypothesis testing approach for finding upper bounds for probabilities that pairs of software components fail simultaneously. International Journal of Reliability, Quality and Safety Engineering (IJRQSE). ISSN 0218-5393. 18(3), p. 209–236. doi: 10.1142/S021853931100410X. Full text in Research Archive
• Kristiansen, Monica; Winther, Rune & Natvig, Bent (2010). Identifying possible rules for selecting the most important component dependencies in compound software. In Ale, Ben; Papazoglou, Ioannis & Zio, Enrico (Ed.), Reliability, risk and safety : back to the future. CRC Press. ISSN 9780415604277. p. 1561–1568. Full text in Research Archive
• Sarshar, Sizarta; Kristiansen, Monica Lind & Sivertsen, Terje (2010). Survey on techniques for modeling of dependencies in the digital I&C design phase. In Society, The American Nuclear Society The American Nuclear (Eds.), Proceedings of the 7th International Topical Meeting on Nuclear Plant Instrumentation, Control and Human-Machine Interface Technologies. American Nuclear Society. ISSN 9780894488436. Full text in Research Archive
• Kristiansen, Monica Lind; Winther, Rune & Natvig, Bent (2010). On component dependencies in compound software. International Journal of Reliability, Quality and Safety Engineering (IJRQSE). ISSN 0218-5393. 17(5), p. 465–493. doi: 10.1142/S0218539310003895. Full text in Research Archive
• Kristiansen, Monica; Winther, Rune & Simensen, John Eldar (2010). Identifying the most important component dependencies in compound software : an experimental study. In Bris, Radim; Martorell, Sebastián & Soares, C. Guedes (Ed.), Reliability, Risk and Safety. Theory and Applications. CRC Press. ISSN 9780415555098. p. 1333–1340. Full text in Research Archive Show summary

  Since it is practically impossible to include all component dependencies in a system?s reliability calculation, a more viable approach would be to include only those dependencies that have a significant impact on the assessed reliability. In this paper, the concepts data-serial and data-paralell components are defined. Then a test system, consisting of five components, is investigated to identify possible rules for selecting the most important component dependencies. To do this, two techniques are applied: 1) direct calculation and 2) Principal Component Analysis (PCA). The results from the analyses clearly show that including partial dependency information may give substantial improvements in the reliability predictions, compared to assuming independence between all software components. However, this is only as long as the most important component dependencies are included in the reliability calculations. It is also apparent that dependencies between data-parallel components are far more important than dependencies between data-serial components. Further the analyses indicate that including only dependencies between data-parallel components may give predictions close to the system?s true failure probability. Including only dependencies between data-serial components may however result in predictions even worse than by assuming independence between all software components.

• Kristiansen, Monica; Winther, Rune; Meulen, Meine van der & Revilla, Miguel A. (2010). The use of metrics to assess software component dependencies. In Soares, Carlos Guedes; Briš, Radim & Martorell, Sebastián (Ed.), Reliability, Risk, and Safety: ESREL 2009. CRC Press. ISSN 9780415555098. p. 1359–1366. Full text in Research Archive Show summary

  In this paper, we present an experimental study which investigates the relations between a set of internal software metrics (McCabe's cyclomatic complexity, Halstead volume, program depth, Source Lines Of Code, etc.) and stochastic failure dependency between software components. The experiment was performed by analysing a large collection of program versions submitted to the same program specification. By analysing the available source code, a set of relevant internal software metrics were calculated for each of the program versions. Additionally, we knew if the program versions would fail or succeed for a large set of possible program inputs. This gave us an ideal situation to study stochastic failure dependencies between software components.

• Kristiansen, Monica; Winther, Rune & Simensen, John Eldar (2009). Identifying the most important component dependencies in compound software : an experimental study. In Bris, Radim; Soares, Carlos Guedes & Martorell, Sebastian (Ed.), Reliability, Risk and Safety – Theory and Applications, (contains papers presented at the 18th European Safety and Reliability Conference (Esrel 2009) in Prague, Czech Republic, September 2009.). CRC Press. ISSN 9780415555098. p. 1333–1340. Full text in Research Archive Show summary

  Predicting the reliability of software systems based on a component approach is inherently difficult, in particular due to failure dependencies between the software components. In this paper we investigate the possibility of including only partial dependency information, i.e. the effect of including only a subset of the actual component dependencies when assessing a system's failure probability. To do this, we have developed a simulator that mimics the failure behaviour of dependent software components. By using the simulator to assess the system's "true'' failure probability, we can compare this to the failure probability predictions we get when various component dependencies are ignored. This makes it possible to evaluate whether partial dependency modelling is worthwhile. In addition, it gives us the possibility to identify the component dependencies that are likely to have the highest impact on the system's predicted failure probability. From the simulation results, we clearly see that ignoring failure dependencies between parallel components have the largest impact on the system's predicted failure probability. Furthermore, our test case indicates that including only dependencies between parallel components might be adequate. In fact, the results from our simulations indicate that including dependencies between components in serial has a more unpredictable effect on the system's predicted failure probability.

• Kristiansen, Monica & Winther, Rune (2007). Assessing reliability of compound software, Risk, reliability and societal safety : proceedings of the European Safety and Reliability Conference 2007 (ESREL 2007). Taylor & Francis. p. 1731–1738. Full text in Research Archive Show summary

  An important challenge when assessing the reliability of compound software is to include dependency aspects in the software reliability models. The objective of this paper is to present an approach on how knowledge on individual software components, as well as their structural usage, can be used to establish upper bounds for failure probabilities of compound software. This is of relevance not only for systems consisting of general in-house software components, but also for systems where pre-developed software components, e.g. COTS, are used. Although there have been several proposed approaches to construct component-based software reliability models (Hamlet 2001; Krishnamurthy et al. 1997, Kuball et al. 1999) most of these approaches tend to ignore the failure dependencies that usually exist between software components. The approach suggested in this paper utilizes Bayesian hypothesis testing principles (Cukic et al. 2003, Kristiansen 2005, Kristiansen et al. 2004; Smidts et al. 2002) which both consider prior information regarding the software components as well as testing.

• Winther, Rune & Kristiansen, Monica (2006). On the modelling of failure dependencies between software components, Safety and reliability for managing risk. Balkema. p. 1443–1450. Full text in Research Archive Show summary

  The problem of assessing reliability of software has been a research topic for more than 30 years, and several successful methods for predicting the reliability of an individual piece of software based on testing have been presented. An important reason why we are still struggling with component-based software reliability methods is that software components rarely can be assumed to fail independently. In this paper, we review some of the component-based approaches that have been proposed, with special emphasis on the handling of dependencies. We then discuss the implications of this problem when assessing reliability of compound software, hoping to provide improved understanding and thus support the development of improved assessment methods.

• Kristiansen, Monica (2005). Finding Upper Bounds for Software Failure Probabilities - Experiments and Results. Lecture Notes in Computer Science (LNCS). ISSN 0302-9743. p. 179–193. Full text in Research Archive Show summary

  Abstract. This paper looks into some aspects of using Bayesian hypothesis testing to find upper bounds for software failure probabilities, which consider prior information regarding the software component in addition to testing. The paper shows how different choices of prior probability distributions for a software component’s failure probability influence the number of tests required to obtain adequate confidence in a software component. In addition, it evaluates different choices of prior probability distributions based on their relevance in a software context. The interpretations of the different prior distributions are emphasised. As a starting point, this paper concentrates on assessment of single software components, but the proposed approachwill later be extended to assess systems consisting of multiple software components. Software components include both general in-house software components, as well as pre-developed software components (e.g. COTS, SOUP, etc).

• Kristiansen, Monica & Winther, Rune (2004). Finding Upper Bounds for Dependencies between Software Components by Using Bayesian Hypothesis Testing. ?. p. 9–15. Full text in Research Archive Show summary

  This paper presents ongoing work on an approach that is intended to make it more feasible to include software component dependencies in software reliability models. By using an approach proposed by Smidts el. al. [11] for estimating an upper bound for a system’s probability of failure on demand (pfd), consisting of statistical testing and Bayesian hypothesis testing, we believe that prior probabilities for simultaneous failures for sets of components can be confirmed with given confidence levels. Using this approach we will possibly, with a comparatively small effort, be able to find upper bounds for the probabilities of simultaneous failures for sets of components, thus making it possible to include dependency aspects in the reliability models. This has relevance not only for general software component models but also for assessing systems where pre-developed software (PDS), e.g. COTS, is used.

View all works in NVA

• Due, Beathe; Kristiansen, Monica; Dang, Ha The Hien & Colomo-Palacios, Ricardo (2015). Introducing Big Data topics: a multicourse experience report from Norway. Full text in Research Archive
• Kristiansen, Monica; Holone, Harald & Natvig, Bent (2014). A Component-based Approach for Assessing Reliability of Compound Software. Full text in Research Archive
• Kristiansen, Monica; Natvig, Bent & Winther, Rune (2013). Assessing software reliability of multistate systems. Full text in Research Archive
• Nätt, Tom Heine; Heide, Christian F. & Kristiansen, Monica (2013). Myter og sannheter om årsaker til studenters prestasjoner i programmering. Full text in Research Archive
• Heide, Christian F.; Kristiansen, Monica Lind & Nätt, Tom Heine (2012). Vurderingsform og karakterbruk. Full text in Research Archive
• Kristiansen, Monica Lind; Natvig, Bent & Winther, Rune (2012). A component-based approach for assessing reliability of compound software. Full text in Research Archive
• Kristiansen, Monica Lind; Winther, Rune & Natvig, Bent (2011). Establishing prior probability distributions for probabilities that pairs of software components fail simultaneously. Full text in Research Archive
• Kristiansen, Monica; Winther, Rune & Natvig, Bent (2010). Identifying possible rules for selecting the most important component dependencies in compound software. Full text in Research Archive Show summary

  Since it is practically impossible to include all component dependencies in a system?s reliability calculation, a more viable approach would be to include only those dependencies that have a significant impact on the assessed reliability. In this paper, the concepts data-serial and data-paralell components are defined. Then a test system, consisting of five components, is investigated to identify possible rules for selecting the most important component dependencies. To do this, two techniques are applied: 1) direct calculation and 2) Principal Component Analysis (PCA). The results from the analyses clearly show that including partial dependency information may give substantial improvements in the reliability predictions, compared to assuming independence between all software components. However, this is only as long as the most important component dependencies are included in the reliability calculations. It is also apparent that dependencies between data-parallel components are far more important than dependencies between data-serial components. Further the analyses indicate that including only dependencies between data-parallel components may give predictions close to the system?s true failure probability. Including only dependencies between data-serial components may however result in predictions even worse than by assuming independence between all software components.

• Kristiansen, Monica; Winther, Rune & Simensen, John Eldar (2009). Identifying the most important component dependencies in compound software: an experimental study. Full text in Research Archive Show summary

  Predicting the reliability of software systems based on a component approach is inherently difficult, in particular due to failure dependencies between the software components. In this paper we investigate the possibility of including only partial dependency information, i.e. the effect of including only a subset of the actual component dependencies when assessing a system's failure probability. To do this, we have developed a simulator that mimics the failure behaviour of dependent software components. By using the simulator to assess the system's "true'' failure probability, we can compare this to the failure probability predictions we get when various component dependencies are ignored. This makes it possible to evaluate whether partial dependency modelling is worthwhile. In addition, it gives us the possibility to identify the component dependencies that are likely to have the highest impact on the system's predicted failure probability. From the simulation results, we clearly see that ignoring failure dependencies between parallel components have the largest impact on the system's predicted failure probability. Furthermore, our test case indicates that including only dependencies between parallel components might be adequate. In fact, the results from our simulations indicate that including dependencies between components in serial has a more unpredictable effect on the system's predicted failure probability.

• Kristiansen, Monica; Winther, Rune; Meulen, Meine van der & Revilla, Miguel A. (2009). The use of metrics to assess software component dependencies. Full text in Research Archive Show summary

  In this paper, we present an experimental study which investigates the relations between a set of internal software metrics (McCabe's cyclomatic complexity, Halstead volume, program depth, Source Lines Of Code, etc.) and stochastic failure dependency between software components. The experiment was performed by analysing a large collection of program versions submitted to the same program specification. By analysing the available source code, a set of relevant internal software metrics were calculated for each of the program versions. Additionally, we knew if the program versions would fail or succeed for a large set of possible program inputs. This gave us an ideal situation to study stochastic failure dependencies between software components.

• Winther, Rune & Kristiansen, Monica (2007). Assessing reliability of compound software. Full text in Research Archive
• Winther, Rune & Kristiansen, Monica (2006). On the modelling of failure dependencies between software components. Full text in Research Archive Show summary

  The problem of assessing reliability of software has been a research topic for more than 30 years, and several successful methods for predicting the reliability of an individual piece of software based on testing have been presented. An important reason why we are still struggling with component-based software reliability methods is that software components rarely can be assumed to fail independently. In this paper, we review some of the component-based approaches that have been proposed, with special emphasis on the handling of dependencies. We then discuss the implications of this problem when assessing reliability of compound software, hoping to provide improved understanding and thus support the development of improved assessment methods.

• Helminen, Atte; Gran, Bjørn Axel; Kristiansen, Monica & Winther, Rune (2004). Use of Operational Data for the Assessment of Pre-Existing Software. Full text in Research Archive Show summary

  To build sufficient confidence on the reliability of the safety systems of nuclear power plants all available sources of information should be used. One important data source is the operational experience collected for the system. The operational experience is particularly applicable for systems of pre-existing software. Even though systems and devices involving pre-existing software are not considered for the functions of highest safety levels of nuclear power plants, they will most probably be introduced to functions of lower safety levels and to none-safety related applications. In the paper we shortly discuss the use of operational experience data for the reliability assessment of pre-existing software in general, and the role of pre-existing software in relation to safety applications. Then we discuss the modelling of operational profiles, the application of expert judgement on operational profiles and the need of a realistic test case. Finally, we discuss the application of operational experience data in Bayesian statistics.

• Kristiansen, Monica & Winther, Rune (2004). Finding Upper Bounds for Dependencies between Software Components by using Bayesian Hypothesis Testing. Full text in Research Archive Show summary

  This paper presents ongoing work on an approach that is intended to make it more feasible to include software component dependencies in software reliability models. Based on an approach proposed by Smidts el. al. [4] for estimating an upper bound for a system’s probability of failure on demand (pfd), we believe that prior probabilities for simultaneous failures for sets of components can be confirmed with given confidence levels. By further development of this approach we will, possibly with a comparatively small effort, be able to find upper bounds for the probabilities of simultaneous failures for sets of components, thus making it possible to include dependency aspects in the reliability models.

• Holone, Monica Kristiansen; Winther, Rune & Natvig, Bent (2011). A Bayesian hypothesis testing approach for finding upper bounds for probabilities that pairs of software components fail simultaneously. Universitetet i Oslo. Full text in Research Archive Show summary

  Predicting the reliability of software systems based on a component-based approach is inherently difficult, in particular due to failure dependencies between software components. One possible way to assess and include dependency aspects in software reliability models is to find upper bounds for probabilities that software components fail simultaneously and then include these into the reliability models. In earlier research, it has been shown that including partial dependency information may give substantial improvements in predicting the reliability of compound software compared to assuming independence between all software components. Furthermore, it has been shown that including dependencies between pairs of data-parallel components may give predictions close to the system's true reliability. In this paper, a Bayesian hypothesis testing approach for finding upper bounds for probabilities that pairs of software components fail simultaneously is described. This approach consists of two main steps: 1) establishing prior probability distributions for probabilities that pairs of software components fail simultaneously and 2) updating these prior probability distributions by performing statistical testing. In this paper, the focus is on the first step in the Bayesian hypothesis testing approach, and two possible procedures for establishing a prior probability distribution for the probability that a pair of software components fails simultaneously are proposed.

• Kristiansen, Monica Lind (2011). A component-based approach for assessing reliability of compound software. Universitetet i Oslo. Full text in Research Archive
• Kristiansen, Monica; Winther, Rune & Natvig, Bent (2010). On component dependencies in compound software. Universitetet i Oslo. Full text in Research Archive Show summary

  Predicting the reliability of software systems based on a component approach is inherently difficult, in particular due to failure dependencies between the software components. Since it is practically difficult to include all component dependencies in a system's reliability calculation, a more viable approach would be to include only those dependencies that have a significant impact on the assessed system reliability. This paper starts out by defining two new concepts: data-serial and data-parallel components. These concepts are illustrated on a simple compound software, and it is shown how dependencies between data-serial and data-parallel components, as well as combinations of these, can be expressed using conditional probabilities. Secondly, this paper illustrates how the components' marginal reliabilities put direct restrictions on the components' conditional probabilities. It is also shown that the degrees of freedom are much fewer than first anticipated when it comes to conditional probabilities. At last, this paper investigates three test cases, each representing a well-known software structure, to identify possible rules for selecting the most important component dependencies. To do this, three different techniques are applied: 1) direct calculation, 2) Birnbaum's measure and 3) Principal Component Analysis (PCA). The results from the analyses clearly show that including partial dependency information may give substantial improvements in the reliability predictions, compared to assuming independence between all software components.

• Winther, Rune & Kristiansen, Monica (2007). Further considerations of dependency aspects in software reliability. Institutt for energiteknikk. Full text in Research Archive Show summary

  In the first part of the report, the problem of modelling software component dependencies when assessing the reliability of compound software is addressed. We suggest that mechanisms that cause dependent failure behaviour can be split into two distinct categories, namely: - Development-cultural aspects (DC-aspects): This includes factors that cause different people, tools, methods, etc. to make the same mistakes. - Structural aspects (S-aspects): This includes factors that allow a failure in one component to affect the execution of another component. We believe that some of the mechanisms can be treated better by more detailed models on the failure mode level, but realize that this will require much more information about the components and the system than is usually assumed available in other methods. In the second part of the report, ideas on how the Bayesian hypothesis testing approach can be extended to assess compound software are discussed further. Through the research, two sources of information relevant for assessing failure dependencies between software components are identified. These are: "Information for assessing the reliabilities of single software components" and "Information on how the software components are used in the compound software". In addition, ideas on how to identify the most important component failure dependencies, i.e. those dependencies that have the most impact on system reliability are presented.

• Kristiansen, Monica & Winther, Rune (2005). Finding upper Bounds for Software Failure Probabilities - Experiments and results. Full text in Research Archive Show summary

  This report looks into some aspects of using Bayesian hypothesis testing to find upper bounds for software failure probabilities. In the first part, the report evaluates the Bayesian hypothesis testing approach for finding upper bounds for failure probabilities of single software components. The report shows how different choices of prior probability distributions for a software component’s failure probability influence the number of tests required to obtain adequate confidence in a software component. In the evaluation, both the effect of the shape of the prior distribution as well as one’s prior confidence in the software component were investigated. In addition, different choices of prior probability distributions are discussed based on their relevance in a software context. In the second part, ideas on how the Bayesian hypothesis testing approach can be extended to assess systems consisting of multiple software components are given. One of the main challenges when assessing systems consisting of multiple software components is to include dependency aspects in the software reliability models. However, different types of failure dependencies between software components must be modelled differently. Identifying different types of failure dependencies are therefore an important condition for choosing a prior probability distribution, which correctly reflects one’s prior belief in the probability for software components failing dependently. In this report, software components include both general in-house software components, as well as pre-developed software components (e.g. COTS, SOUP, etc).

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