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  • 1.
    Al Mamun, Md Abdullah
    et al.
    Department of Computer Science and Engineering, Chalmers / University of Gothenburg, Sweden.
    Berger, Christian
    Department of Computer Science and Engineering, Chalmers / University of Gothenburg, Sweden.
    Hansson, Jörgen
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Effects of measurements on correlations of software code metrics2019Ingår i: Empirical Software Engineering, ISSN 1382-3256, E-ISSN 1573-7616, Vol. 24, nr 4, s. 2764-2818Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Context

    Software metrics play a significant role in many areas in the life-cycle of software including forecasting defects and foretelling stories regarding maintenance, cost, etc. through predictive analysis. Many studies have found code metrics correlated to each other at such a high level that such correlated code metrics are considered redundant, which implies it is enough to keep track of a single metric from a list of highly correlated metrics.

    Objective

    Software is developed incrementally over a period. Traditionally, code metrics are measured cumulatively as cumulative sum or running sum. When a code metric is measured based on the values from individual revisions or commits without consolidating values from past revisions, indicating the natural development of software, this study identifies such a type of measure as organic. Density and average are two other ways of measuring metrics. This empirical study focuses on whether measurement types influence correlations of code metrics.

    Method

    To investigate the objective, this empirical study has collected 24 code metrics classified into four categories, according to the measurement types of the metrics, from 11,874 software revisions (i.e., commits) of 21 open source projects from eight well-known organizations. Kendall’s τ -B is used for computing correlations. To determine whether there is a significant difference between cumulative and organic metrics, Mann-Whitney U test, Wilcoxon signed rank test, and paired-samples sign test are performed.

    Results

    The cumulative metrics are found to be highly correlated to each other with an average coefficient of 0.79. For corresponding organic metrics, it is 0.49. When individual correlation coefficients between these two measure types are compared, correlations between organic metrics are found to be significantly lower (with p <0.01) than cumulative metrics. Our results indicate that the cumulative nature of metrics makes them highly correlated, implying cumulative measurement is a major source of collinearity between cumulative metrics. Another interesting observation is that correlations between metrics from different categories are weak.

    Conclusions

    Results of this study reveal that measurement types may have a significant impact on the correlations of code metrics and that transforming metrics into a different type can give us metrics with low collinearity. These findings provide us a simple understanding how feature transformation to a different measurement type can produce new non-collinear input features for predictive models.

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  • 2.
    Grindal, Mats
    et al.
    Högskolan i Skövde, Institutionen för kommunikation och information. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Lindström, Birgitta
    Högskolan i Skövde, Institutionen för kommunikation och information. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    Offutt, Jeff
    George Mason Univ, Dept Informat & Software Engn, Fairfax, VA 22030 USA.
    Andler, Sten F.
    Högskolan i Skövde, Institutionen för kommunikation och information. Högskolan i Skövde, Forskningscentrum för Informationsteknologi.
    An Evaluation of Combination Strategies for Test Case Selection2006Ingår i: Empirical Software Engineering, ISSN 1382-3256, E-ISSN 1573-7616, Vol. 11, nr 4, s. 583-611Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    This paper presents results from a comparative evaluation of five combination strategies. Combination strategies are test case selection methods that combine “interesting” values of the input parameters of a test subject to form test cases. This research comparatively evaluated five combination strategies; the All Combination strategy (AC), the Each Choice strategy (EC), the Base Choice strategy (BC), Orthogonal Arrays (OA) and the algorithm from the Automatic Efficient Test Generator (AETG). AC satisfies n-wise coverage, EC and BC satisfy 1-wise coverage, and OA and AETG satisfy pair-wise coverage. The All Combinations strategy was used as a “gold standard” strategy; it subsumes the others but is usually too expensive for practical use. The others were used in an experiment that used five programs seeded with 128 faults. The combination strategies were evaluated with respect to the number of test cases, the number of faults found, failure size, and number of decisions covered. The strategy that requires the least number of tests, Each Choice, found the smallest number of faults. Although the Base Choice strategy requires fewer test cases than Orthogonal Arrays and AETG, it found as many faults. Analysis also shows some properties of the combination strategies that appear significant. The two most important results are that the Each Choice strategy is unpredictable in terms of which faults will be revealed, possibly indicating that faults are found by chance, and that the Base Choice and the pair-wise combination strategies to some extent target different types of faults.

  • 3.
    Heeager, Lise Tordrup
    et al.
    Department of Business Administration, Aarhus University, Denmark.
    Rose, Jeremy
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningscentrum för Informationsteknologi. Department of Computer Science, Aalborg University, Aalborg, Denmark.
    Optimising agile development practices for the maintenance operation: nine heuristics2015Ingår i: Empirical Software Engineering, ISSN 1382-3256, E-ISSN 1573-7616, Vol. 20, nr 6, s. 1762-1784Artikel i tidskrift (Refereegranskat)
  • 4.
    Robles, Gregorio
    et al.
    Department of Telematic and Computational Systems Engineering, Universidad Rey Juan Carlos, Madrid, Spain.
    Capiluppi, Andrea
    Department of Computer Science, University of Groningen, The Netherlands.
    Gonzalez-Barahona, Jesus M.
    Department of Telematic and Computational Systems Engineering, Universidad Rey Juan Carlos, Madrid, Spain.
    Lundell, Björn
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningsmiljön Informationsteknologi.
    Gamalielsson, Jonas
    Högskolan i Skövde, Institutionen för informationsteknologi. Högskolan i Skövde, Forskningsmiljön Informationsteknologi.
    Development effort estimation in free/open source software from activity in version control systems2022Ingår i: Empirical Software Engineering, ISSN 1382-3256, E-ISSN 1573-7616, Vol. 27, artikel-id 135Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Effort estimation models are a fundamental tool in software management, and used as a fore-cast for resources, constraints and costs associated to software development. For Free/OpenSource Software (FOSS) projects, effort estimation is especially complex: professionaldevelopers work alongside occasional, volunteer developers, so the overall effort (in person-months) becomes non-trivial to determine. The objective of this work it to develop a simpleeffort estimation model for FOSS projects, based on the historic data of developers’ effort.The model is fed with direct developer feedback to ensure its accuracy. After extractingthe personal development profiles of several thousands of developers from 6 large FOSSprojects, we asked them to fill in a questionnaire to determine if they should be consideredas full-time developers in the project that they work in. Their feedback was used to fine-tune the value of an effort threshold, above which developers can be considered as full-time.With the help of the over 1,000 questionnaires received, we were able to determine, for every project in our sample, the threshold of commits that separates full-time from non-full-time developers. We finally offer guidelines and a tool to apply our model to FOSS projects that use a version control system.

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