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- Badampudi, Deepika, 1984-
(författare)
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Towards decision-making to choose among different component origins
- 2016
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Licentiatavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Context: The amount of software in solutions provided in various domains is continuously growing. These solutions are a mix of hardware and software solutions, often referred to as software-intensive systems. Companies seek to improve the software development process to avoid delays or cost overruns related to the software development. Objective: The overall goal of this thesis is to improve the software development/building process to provide timely, high quality and cost efficient solutions. The objective is to select the origin of the components (in-house, outsource, components off-the-shelf (COTS) or open source software (OSS)) that facilitates the improvement. The system can be built of components from one origin or a combination of two or more (or even all) origins. Selecting a proper origin for a component is important to get the most out of a component and to optimize the development. Method: It is necessary to investigate the component origins to make decisions to select among different origins. We conducted a case study to explore the existing challenges in software development. The next step was to identify factors that influence the choice to select among different component origins through a systematic literature review using a snowballing (SB) strategy and a database (DB) search. Furthermore, a Bayesian synthesis process is proposed to integrate the evidence from literature into practice. Results: The results of this thesis indicate that the context of software-intensive systems such as domain regulations hinder the software development improvement. In addition to in-house development, alternative component origins (outsourcing, COTS, and OSS) are being used for software development. Several factors such as time, cost and license implications influence the selection of component origins. Solutions have been proposed to support the decision-making. However, these solutions consider only a subset of factors identified in the literature. Conclusions: Each component origin has some advantages and disadvantages. Depending on the scenario, one component origin is more suitable than the others. It is important to investigate the different scenarios and suitability of the component origins, which is recognized as future work of this thesis. In addition, the future work is aimed at providing models to support the decision-making process.
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| 2. |
- Madeyski, Lech, et al.
(författare)
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Detecting code smells using industry-relevant data
- 2023
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Ingår i: Information and Software Technology. - : Elsevier. - 0950-5849 .- 1873-6025. ; 155
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Tidskriftsartikel (refereegranskat)abstract
- Context: Code smells are patterns in source code associated with an increased defect rate and a higher maintenance effort than usual, but without a clear definition. Code smells are often detected using rules hard-coded in detection tools. Such rules are often set arbitrarily or derived from data sets tagged by reviewers without the necessary industrial know-how. Conclusions from studying such data sets may be unreliable or even harmful, since algorithms may achieve higher values of performance metrics on them than on models tagged by experts, despite not being industrially useful. Objective: Our goal is to investigate the performance of various machine learning algorithms for automated code smell detection trained on code smell data set(MLCQ) derived from actively developed and industry-relevant projects and reviews performed by experienced software developers. Method: We assign the severity of the smell to the code sample according to a consensus between the severities assigned by the reviewers, use the Matthews Correlation Coefficient (MCC) as our main performance metric to account for the entire confusion matrix, and compare the median value to account for non-normal distributions of performance. We compare 6720 models built using eight machine learning techniques. The entire process is automated and reproducible. Results: Performance of compared techniques depends heavily on analyzed smell. The median value of our performance metric for the best algorithm was 0.81 for Long Method, 0.31 for Feature Envy, 0.51 for Blob, and 0.57 for Data Class. Conclusions: Random Forest and Flexible Discriminant Analysis performed the best overall, but in most cases the performance difference between them and the median algorithm was no more than 10% of the latter. The performance results were stable over multiple iterations. Although the F-score omits one quadrant of the confusion matrix (and thus may differ from MCC), in code smell detection, the actual differences are minimal. © 2022 Elsevier B.V.
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| 4. |
- Madeyski, Lech, et al.
(författare)
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Overcoming the equivalent mutant problem : A systematic literature review and a comparative experiment of second order mutation
- 2014
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Ingår i: IEEE Transactions on Software Engineering. - : IEEE. - 0098-5589 .- 1939-3520. ; 40:1, s. 23-42
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Tidskriftsartikel (refereegranskat)abstract
- Context. The equivalent mutant problem (EMP) is one of the crucial problems in mutation testing widely studied over decades. Objectives. The objectives are: to present a systematic literature review (SLR) in the field of EMP; to identify, classify and improve the existing, or implement new, methods which try to overcome EMP and evaluate them. Method. We performed SLR based on the search of digital libraries. We implemented four second order mutation (SOM) strategies, in addition to first order mutation (FOM), and compared them from different perspectives. Results. Our SLR identified 17 relevant techniques (in 22 articles) and three categories of techniques: detecting (DEM); suggesting (SEM); and avoiding equivalent mutant generation (AEMG). The experiment indicated that SOM in general and JudyDiffOp strategy in particular provide the best results in the following areas: total number of mutants generated; the association between the type of mutation strategy and whether the generated mutants were equivalent or not; the number of not killed mutants; mutation testing time; time needed for manual classification. Conclusions. The results in the DEM category are still far from perfect. Thus, the SEM and AEMG categories have been developed. The JudyDiffOp algorithm achieved good results in many areas.
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