Browsing University of Hertfordshire by Author "Bowes, David"

Now showing items 1-20 of 22

    • Authors' Reply to “Comments on 'Researcher Bias: : The Use of Machine Learning in Software Defect Prediction 

      Shepperd, Martin; Hall, Tracy; Bowes, David (2018-11)
      In 2014 we published a meta-analysis of software defect prediction studies [1]. This suggested that the most important factor in determining results was Research Group i.e., who conducts the experiment is more important ...

    • Building an Ensemble for Software Defect Prediction Based on Diversity Selection 

      Petri, Jean; Bowes, David; Hall, Tracy; Christianson, Bruce; Baddoo, Nathan (ACM Press, 2016-09-09)
      Background: Ensemble techniques have gained attention in various scientific fields. Defect prediction researchers have investigated many state-of-the-art ensemble models and concluded that in many cases these outperform ...

    • Comparing the performance of fault prediction models which report multiple performance measures : recomputing the confusion matrix 

      Bowes, David; Hall, Tracy; Gray, David (ACM Press, 2012)
      There are many hundreds of fault prediction models published in the literature. The predictive performance of these models is often reported using a variety of different measures. Most performance measures are not directly ...

    • DConfusion: A technique to allow cross study performance evaluation of fault prediction studies. 

      Bowes, David; Hall, Tracy; Gray, David (2014-04)
      There are many hundreds of fault prediction models published in the literature. The predictive performance of these models is often reported using a variety of different measures. Most performance measures are not directly ...

    • Developing Fault-Prediction Models : What the research can show industry 

      Hall, Tracy; Beecham, Sarah; Bowes, David; Gray, David; Counsell, Steve (2011)
      A systematic review of the research literature on fault-prediction models from 2000 through 2010 identified 36 studies that sufficiently defined their models and development context and methodology. The authors quantitatively ...

    • Different classifiers find different defects although with different level of consistency 

      Bowes, David; Hall, Tracy; Petrić, Jean (ACM Press, 2015-10-21)
      BACKGROUND - During the last 10 years hundreds of different defect prediction models have been published. The performance of the classifiers used in these models is reported to be similar with models rarely performing above ...

    • Editorial 

      Sarro, Federica; Hall, Tracy; Baddoo, Nathan; Buckley, Jim; English, Michael; Counsell, Steve; Noll, John; Rainer, Austen; Jan-Stol, Klaas; O'Connor, Dara; Power, Norah; Bowes, David; Abrahao, Silvia; Fontana, Francesca Arcelli; Avgeriou, Paris; Ali, Nour; Babar, M. Ali; Baldassarre, Maria Teresa; Biffl, Stefan; Brereton, O. Pearl; Budgen, David; Çaglayan, Bora; Cain, James; Caivano, Danilo; Capiluppi, Andrea; Carver, Jeffrey; Charters, Stuart; Ciolkowski, Marcus; Clear, Tony; Cruzes, Daniela S.; Daneva, Maya; Dieste, Oscar; Dybå, Tore; Falessi, Davide; Ferrucci, Filomena; Franch, Xavier; Galster, Matthias; Gatrell, Matt; Genero, Marcela; Graziotin, Daniel; Harrison, Rachel; Herold, Sebastian; Höst, Martin; Ikram, Naveed; Keung, Jacky; Krinke, Jens; Kuhrmann, Marco; Licorish, Sherlock; Madeyski, Lech; Mendes, Emilia; Méndez Fernández, Daniel; Menzies, Tim; Morasca, Sandro; Müller, Matthias; Münch, Jürgen; Nagappan, Nachiappan; Niazi, Mahmood; Penzenstadler, Birgit; Pfahl, Dietmar; Pfahl, Dietmar; Prechelt, Lutz; Radlinski, Lukasz; Ralph, Paul; Rodríguez, Daniel; Scanniello, Giuseppe; Sharp, Helen; Shepperd, Martin; Da Silva, Fabio Q B; Sjøberg, Dag I K; Solari, Martin; Staples, Mark; Stol, Klaas Jan; Svahnberg, Mikael; Torchiano, Marco; Travassos, Guilherme; Visaggio, Giuseppe; Wagner, Stefan; Winkler, Dietmar; Wnuk, Krzysztof; Wnuk, Krzysztof; Wood, Murray; Yamashita, Aiko; Zhang, He; França, Breno; Martínez-Fernández, Silverio; Meshesha, Fitsum; Mindermann, Kai; Niedermayr, Rainer; Oyetoyan, Tosin Daniel; Ribeiro, Talita; Stade, Melanie; Zahedi, Mansooreh; Zanoni, Marco (ACM Press, 2016-06-01)

    • Evaluating Three Approaches to Extracting Fault Data from Software Change Repositories 

      Hall, Tracy; Bowes, David; Liebchen, Gernot; Wernick, Paul (Springer Nature, 2010)
      Software products can only be improved if we have a good understanding of the faults they typically contain. Code faults are a significant source of software product problems which we currently do not understand sufficiently. ...

    • Further thoughts on precision 

      Gray, D.; Bowes, David; Davey, Neil; Sun, Yi; Christianson, B. (2011)
      Background: There has been much discussion amongst automated software defect prediction researchers regarding use of the precision and false positive rate classifier performance metrics. Aim: To demonstrate and explain why ...

    • Further thoughts on precision 

      Gray, D.; Bowes, David; Davey, N.; Sun, Yi; Christianson, B. (2011)
      Background: There has been much discussion amongst automated software defect prediction researchers regarding use of the precision and false positive rate classifier performance metrics. Aim: To demonstrate and explain why ...

    • The inconsistent measurement of Message Chains 

      Bowes, David; Randall, David; Hall, Tracy (Institute of Electrical and Electronics Engineers (IEEE), 2013)
      Fowler and Beck defined 22 Code Bad Smells. These smells are useful indicators of code that may need to be refactored. A range of tools have been developed that measure smells in Java code. We aim to compare the results ...

    • The misuse of the NASA metrics data program data sets for automated software defect prediction 

      Gray, D.; Bowes, David; Davey, N.; Sun, Yi; Christianson, B. (Institution of Engineering and Technology (IET), 2011)
      Background: The NASA Metrics Data Program data sets have been heavily used in software defect prediction experiments. Aim: To demonstrate and explain why these data sets require significant pre-processing in order to be ...

    • Mutation-aware fault prediction 

      Bowes, David; Hall, Tracy; Harman, Mark; Jia, Yue; Sarro, Federica; Wu, Fan (ACM Press, 2016-07-18)
      We introduce mutation-aware fault prediction, which leverages additional guidance from metrics constructed in terms of mutants and the test cases that cover and detect them. We report the results of 12 sets of experiments, ...

    • The Relationship between Evolutionary Coupling and Defects in Large Industrial Software 

      Kirbas, Serkan; Caglayan, Bora; Hall, Tracy; Counsell, Steve; Bowes, David; Sen, Alper; Bener, Ayse (2017-04-05)
      Evolutionary coupling (EC) is defined as the implicit relationship between 2 or more software artifacts that are frequently changed together. Changing software is widely reported to be defect-prone. In this study, we ...

    • Reproducibility and Replicability of Software Defect Prediction Studies 

      Mahmood, Zaheed; Bowes, David; Hall, Tracy; Lane, Peter; Petric, Jean (2018-07-01)
      Context: Replications are an important part of scientific disciplines. Replications test the credibility of original studies and can separate true results from those that are unreliable. Objective: In this paper we investigate ...

    • Researcher bias : The use of machine learning in software defect prediction 

      Shepperd, Martin; Bowes, David; Hall, Tracy (2014-06-03)
      Background. The ability to predict defect-prone software components would be valuable. Consequently, there have been many empirical studies to evaluate the performance of different techniques endeavouring to accomplish ...

    • SLuRp: a tool to help large complex systematic literature reviews deliver valid and rigorous results 

      Bowes, David; Hall, Tracy; Beecham, Sarah (ACM Press, 2012)
      Background: Systematic literature reviews are increasingly used in software engineering. Most systematic literature reviews require several hundred papers to be examined and assessed. This is not a trivial task and can be ...

    • Software defect prediction using static code metrics underestimates defect-proneness 

      Gray, David; Bowes, David; Davey, N.; Sun, Yi; Christianson, B. (Institute of Electrical and Electronics Engineers (IEEE), 2010)
      Many studies have been carried out to predict the presence of software code defects using static code metrics. Such studies typically report how a classifier performs with real world data, but usually no analysis of the ...

    • Software defect prediction: do different classifiers find the same defects? 

      Bowes, David; Hall, Tracy; petric, Jean (2017-02-07)
      During the last 10 years, hundreds of different defect prediction models have been published. The performance of the classifiers used in these models is reported to be similar with models rarely performing above the ...

    • Some code smells have a significant but small effect on faults 

      Hall, Tracy; Zhang, Min; Bowes, David; Sun, Yi (2014-08)
      We investigate the relationship between faults and five of Fowler et al.'s least-studied smells in code: Data Clumps, Switch Statements, Speculative Generality, Message Chains, and Middle Man. We developed a tool to detect ...