Using antipatterns to improve the quality of FLOSS development

Antipatterns have been mostly reported in closed source software environments. With the advent of Free/Libre Open Source Software (FLOSS), researchers have started analysing popular FLOSS projects, seeking vitality indicators and success patterns.  However, an impressively high percentage of FLOSS projects are unsuccessful.  Moreover, even in the successful cases of FLOSS there can be found tracks of failed attempts, dead-ends, forks, abandonments etc.  FLOSS antipatterns can help developers to improve their code and improve the communication and collaboration within the FLOSS community.  In this paper, we present some example of FLOSS antipatterns and discuss the benefits that they bring to various FLOSS user roles.  Furthermore, we present ontology-based technology and software tools that can be used to assist FLOSS developers and community users to identify, document, share antipatterns and use these mechanisms to assist FLOSS projects conform to specified requirements.  Finally, we propose a framework for the quantitative identification of the antipatterns to use as quality indicators in the certification of FLOSS products

1 Sharing Bad Practices in Design to Improve the Use of Patterns

To ensure the use of good analysis and design practices and an easier maintenance of software, analysts and designers may use patterns. To help them, we propose models inspection in order to detect instantiations of “spoiled pattern ” and models reworking through the use of the design patterns. As a design pattern allows the instantiation of the best known solution for a given problem, a “spoiled pattern ” allows the instantiation of alternative solutions for the same problem: requirements are respected, but architecture is improvable. We have collected a set of alternative solutions and deduced the corresponding spoiled patterns. We have defined a first catalog of these improvable practices from several experiments with students. To overcome the limits imposed by this method (restricted public, limited problems and tiresome validation process), we would like to open this problematic to the expert community. Therefore, we propose a collaborative website sharing bad practices in object oriented design to improve the use of patterns

Antipatterns in software classification taxonomies

Empirical results in software engineering have long started to show that findings are unlikely to be applicable to all software systems, or any domain: results need to be evaluated in specified contexts, and limited to the type of systems that they were extracted from. This is a known issue, and requires the establishment of a classification of software types. This paper makes two contributions: the first is to evaluate the quality of the current software classifications landscape. The second is to perform a case study showing how to create a classification of software types using a curated set of software systems. Our contributions show that existing, and very likely even new, classification attempts are deemed to fail for one or more issues, that we named as the ‘antipatterns’ of software classification tasks. We collected 7 of these antipatterns that emerge from both our case study, and the existing classifications. These antipatterns represent recurring issues in a classification, so we discuss practical ways to help researchers avoid these pitfalls. It becomes clear that classification attempts must also face the daunting task of formulating a taxonomy of software types, with the objective of establishing a hierarchy of categories in a classification

Fundamental Approaches to Software Engineering

This open access book constitutes the proceedings of the 23rd International Conference on Fundamental Approaches to Software Engineering, FASE 2020, which took place in Dublin, Ireland, in April 2020, and was held as Part of the European Joint Conferences on Theory and Practice of Software, ETAPS 2020. The 23 full papers, 1 tool paper and 6 testing competition papers presented in this volume were carefully reviewed and selected from 81 submissions. The papers cover topics such as requirements engineering, software architectures, specification, software quality, validation, verification of functional and non-functional properties, model-driven development and model transformation, software processes, security and software evolution

Generic Quality-Aware Refactoring and Co-Refactoring in Heterogeneous Model Environments

Software has been subject to change, at all times, in order to make parts of it, for instance, more reusable, better to understand by humans, or to increase efficiency under a certain point of view. Restructurings of existing software can be complex. To prevent developers from doing this manually, they got tools at hand being able to apply such restructurings automatically. These automatic changes of existing software to improve quality while preserving its behaviour is called refactoring. Refactoring is well investigated for programming languages and mature tools exist for executing refactorings in integrated development environments (IDEs). In recent years, the development paradigm of Model-Driven Software Development (MDSD) became more and more popular and we experience a shift in the sense that development artefacts are considered as models which conform metamodels. This can be understood as abstraction, which resulted in the trend that a plethora of new so-called model-based Domain-Specific Languages (DSLs) arose. DSLs have become an integral part in the MDSD and it is obvious that models are subject to change, as well. Thus, refactoring support is required for DSLs in order to prevent users from doing it manually. The problem is that the amount of DSLs is huge and refactorings should not be implemented for new for each of them, since they are quite similar from an abstract viewing. Existing approaches abstract from the target language, which is not flexible enough because some assumptions about the languages have to be made and arbitrary DSLs are not supported. Furthermore, the relation between a strategy which finds model deficiencies that should be improved, a resolving refactoring, and the improved quality is only implicit. Focussing on a particular quality and only detecting those deficiencies deteriorating this quality is difficult, and elements of detected deficient structures cannot be referred to in the resolving refactoring. In addition, heterogeneous models in an IDE might be connected physically or logically, thus, they are dependent. Finding such connections is difficult and can hardly be achieved manually. Applying a restructuring in a model implied by a refactoring in a dependent model must also be a refactoring, in order to preserve the meaning. Thus, this kind of dependent refactorings require an appropriate abstraction mechanism, since they must be specified for dependent models of different DSLs. The first contribution, Role-Based Generic Model Refactoring, uses role models to abstract from refactorings instead of the target languages. Thus, participating structures in a refactoring can be specified generically by means of role models. As a consequence, arbitrary model-based DSLs are supported, since this approach does not make any assumptions regarding the target languages. Our second contribution, Role-Based Quality Smells, is a conceptual framework and correlates deficiencies, their deteriorated qualities, and resolving refactorings. Roles are used to abstract from the causing structures of a deficiency, which then are subject to resolving refactorings. The third contribution, Role-Based Co-Refactoring, employs the graph-logic isomorphism to detect dependencies between models. Dependent refactorings, which we call co-refactorings, are specified on the basis of roles for being independent from particular target DSLs. All introduced concepts are implemented in our tool Refactory. An evaluation in different scenarios complements the thesis. It shows that role models emerged as very powerful regarding the reuse of generic refactorings in arbitrary languages. Role models are suited as an interface for certain structures which are to be refactored, scanned for deficiencies, or co-refactored. All of the presented approaches benefit from it.:List of Figures xv List of Tables xvii List of Listings xix 1. Introduction 1 1.1. Language-Tool Generation Without Consideration Of Time And Space . . . . . 4 1.2. Challenges . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 1.3. Generic Quality-Aware Refactoring and Co-Refactoring in Heterogeneous Model Environments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 10 2. Foundations 15 2.1. Refactoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 2.2. Model-Driven Software Development . . . . . . . . . . . . . . . . . . . . . . . . 16 2.2.1. Levels of Abstraction and Metamodelling . . . . . . . . . . . . . . . . . 17 2.2.2. Model Transformations . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.3. Role-Based Modelling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 3. Related Work 23 3.1. Model Refactoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 3.1.1. Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 3.1.2. Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 3.1.3. Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.2. Determination of Quality-Related De ciencies . . . . . . . . . . . . . . . . . . . 32 3.2.1. Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 3.2.2. Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34 3.2.3. Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 3.3. Co-Refactoring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 3.3.1. Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 3.3.2. Literature Review . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40 3.3.3. Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 3.4. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 4. Role-Based Generic Model Refactoring 51 4.1. Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51 4.2. Specifying Generic Refactorings with Role Models . . . . . . . . . . . . . . . . . 53 4.2.1. Specifying Structural Constraints using Role Models . . . . . . . . . . . 55 4.2.2. Mapping Roles to Language Concepts Using Role Mappings . . . . . . . 57 4.2.3. Specifying Language-Independent Transformations using Refactoring Speci cations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60 4.2.4. Composition of Refactorings . . . . . . . . . . . . . . . . . . . . . . . . . 67 4.3. Preserving Semantics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 4.4. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71 5. Suggesting Role Mappings as Concrete Refactorings 73 5.1. Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 5.2. Automatic Derivation of Suggestions for Role Mappings with Graph Querying . 74 5.3. Reduction of the Number of Valid Matches . . . . . . . . . . . . . . . . . . . . . 76 5.4. Comparison to Model Matching . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 5.5. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 78 6. Role-Based Quality Smells as Refactoring Indicator 79 6.1. Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 6.2. Correlating Model De ciencies, Qualities and Refactorings . . . . . . . . . . . . 80 6.2.1. Quality Smell Repository . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 6.2.2. Quality Smell Calculation Repository . . . . . . . . . . . . . . . . . . . . 85 6.3. Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87 6.4. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88 7. A Quality Smell Catalogue for Android Applications 89 7.1. Quality Smell Catalogue Schema . . . . . . . . . . . . . . . . . . . . . . . . . . . 89 7.2. Acquiring Quality Smells . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 7.3. Structure-Based Quality Smells—A Detailed Example . . . . . . . . . . . . . . . 92 7.3.1. The Pattern Language . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92 7.3.2. Quality Smell: Interruption from Background . . . . . . . . . . . . . . . 93 7.4. Quality Smells for Android Applications . . . . . . . . . . . . . . . . . . . . . . 96 7.4.1. Quality Smell: Data Transmission Without Compression . . . . . . . . . 96 7.4.2. Quality Smell: Dropped Data . . . . . . . . . . . . . . . . . . . . . . . . 98 7.4.3. Quality Smell: Durable WakeLock . . . . . . . . . . . . . . . . . . . . . 98 7.4.4. Quality Smell: Internal Use of Getters/Setters . . . . . . . . . . . . . . . 99 7.4.5. Quality Smell: No Low Memory Resolver . . . . . . . . . . . . . . . . . 101 7.4.6. Quality Smell: Rigid AlarmManager . . . . . . . . . . . . . . . . . . . . 101 7.4.7. Quality Smell: Unclosed Closeable . . . . . . . . . . . . . . . . . . . . . 102 7.4.8. Quality Smell: Untouchable . . . . . . . . . . . . . . . . . . . . . . . . . 103 7.5. Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104 8. Role-Based Co-Refactoring in Multi-Language Development Environments 105 8.1. Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105 8.2. Example . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 107 8.3. Dependency Knowledge Base . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108 8.3.1. Categories of Model Dependencies . . . . . . . . . . . . . . . . . . . . . 108 8.3.2. When to Determine Model Dependencies . . . . . . . . . . . . . . . . . 110 8.3.3. How to Determine Model Dependencies . . . . . . . . . . . . . . . . . . 111 8.4. Co-Refactoring Knowledge Base . . . . . . . . . . . . . . . . . . . . . . . . . . . 113 8.4.1. Specifying Coupled Refactorings with Co-Refactoring Speci cations . . 114 8.4.2. Specifying Bindings for Co-Refactorings . . . . . . . . . . . . . . . . . . 116 8.4.3. Determination of Co-Refactoring Speci cations . . . . . . . . . . . . . . 118 8.5. Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 118 8.6. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119 9. Refactory: An Eclipse Tool For Quality-Aware Refactoring and Co-Refactoring 121 9.1. Refactoring Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122 9.1.1. Role Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 122 9.1.2. Refactoring Speci cation . . . . . . . . . . . . . . . . . . . . . . . . . . . 124 9.1.3. Role Model Mapping . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 126 9.1.4. Refactoring Composition . . . . . . . . . . . . . . . . . . . . . . . . . . . 128 9.1.5. Custom Refactoring Extensions . . . . . . . . . . . . . . . . . . . . . . . 129 9.1.6. Pre- and Post-conditions . . . . . . . . . . . . . . . . . . . . . . . . . . . 129 9.1.7. Integration Into the Eclipse Refactoring Framework . . . . . . . . . . . . 130 9.2. Quality Smell Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 134 9.3. Co-Refactoring Framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137 9.3.1. Concrete Syntax of a CoRefSpec . . . . . . . . . . . . . . . . . . . . . . . 138 9.3.2. Expression Evaluation by Using an Expression Language . . . . . . . . . 138 9.3.3. UI and Integration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140 9.4. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 141 10. Evaluation 143 10.1. Case Study: Reuse of Generic Refactorings in many DSLs . . . . . . . . . . . . . 143 10.1.1. Threats to validity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143 10.1.2. Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144 10.1.3. Experience Report . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 146 10.2. Case Study: Suggestion of Valid Role Mappings . . . . . . . . . . . . . . . . . . 147 10.2.1. Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147 10.2.2. Evaluation and Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . 151 10.3. Proof of Concept: Co-Refactoring OWL and Ecore Models . . . . . . . . . . . . 155 10.3.1. Coupled OWL-Ecore Refactorings . . . . . . . . . . . . . . . . . . . . . 156 10.3.2. Realisation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 157 10.3.3. Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160 11. Summary, Conclusion and Outlook 161 11.1. Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161 11.2. Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163 11.3. Outlook . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166 Appendix 169 A. List of Role Models . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 169 B. Comparison to Role Feature Model . . . . . . . . . . . . . . . . . . . . . . . . . 171 C. Complete List of Role Mappings . . . . . . . . . . . . . . . . . . . . . . . . . . . 173 D. List of all IncPL Patterns for Detecting Quality Smells . . . . . . . . . . . . . . . 176 E. Post-Processor of the Extract CompositeState refactoring for UML State Machines 183 F. Speci cation of the Conference Language . . . . . . . . . . . . . . . . . . . . . . 185 List of Abbreviations 187 Bibliography 19

Analyzing Clone Evolution for Identifying the Important Clones for Management

Code clones (identical or similar code fragments in a code-base) have dual but contradictory impacts (i.e., both positive and negative impacts) on the evolution and maintenance of a software system. Because of the negative impacts (such as high change-proneness, bug-proneness, and unintentional inconsistencies), software researchers consider code clones to be the number one bad-smell in a code-base. Existing studies on clone management suggest managing code clones through refactoring and tracking. However, a software system's code-base may contain a huge number of code clones, and it is impractical to consider all these clones for refactoring or tracking. In these circumstances, it is essential to identify code clones that can be considered particularly important for refactoring and tracking. However, no existing study has investigated this matter. We conduct our research emphasizing this matter, and perform five studies on identifying important clones by analyzing clone evolution history. In our first study we detect evolutionary coupling of code clones by automatically investigating clone evolution history from thousands of commits of software systems downloaded from on-line SVN repositories. By analyzing evolutionary coupling of code clones we identify a particular clone change pattern, Similarity Preserving Change Pattern (SPCP), such that code clones that evolve following this pattern should be considered important for refactoring. We call these important clones the SPCP clones. We rank SPCP clones considering their strength of evolutionary coupling. In our second study we further analyze evolutionary coupling of code clones with an aim to assist clone tracking. The purpose of clone tracking is to identify the co-change (i.e. changing together) candidates of code clones to ensure consistency of changes in the code-base. Our research in the second study identifies and ranks the important co-change candidates by analyzing their evolutionary coupling. In our third study we perform a deeper analysis on the SPCP clones and identify their cross-boundary evolutionary couplings. On the basis of such couplings we separate the SPCP clones into two disjoint subsets. While one subset contains the non-cross-boundary SPCP clones which can be considered important for refactoring, the other subset contains the cross-boundary SPCP clones which should be considered important for tracking. In our fourth study we analyze the bug-proneness of different types of SPCP clones in order to identify which type(s) of code clones have high tendencies of experiencing bug-fixes. Such clone-types can be given high priorities for management (refactoring or tracking). In our last study we analyze and compare the late propagation tendencies of different types of code clones. Late propagation is commonly regarded as a harmful clone evolution pattern. Findings from our last study can help us prioritize clone-types for management on the basis of their tendencies of experiencing late propagations. We also find that late propagation can be considerably minimized by managing the SPCP clones. On the basis of our studies we develop an automatic system called AMIC (Automatic Mining of Important Clones) that identifies the important clones for management (refactoring and tracking) and ranks these clones considering their evolutionary coupling, bug-proneness, and late propagation tendencies. We believe that our research findings have the potential to assist clone management by pin-pointing the important clones to be managed, and thus, considerably minimizing clone management effort