The source code of a Java-based software system is often structured into packages. When packages are large, they often carry maintainability quality issues. In the literature, there is a lack of empirical evidence on the specific maintainability issues that occur when packages become too large. Our study fills this gap by performing relationship analysis of package size with respect to internal maintainability characteristics (coupling, cohesion, and complexity) using package-level metrics collected from 111 open-source Java projects provided in Qualitas Corpus . Our results show significantly higher maintainability issues in large packages as indicated by the maintainability metrics. We also report strong relationships of package size with cohesion (represented by the number of connected components in a package) and complexity (measured by the number of internal relationships in a package). Based on these strong associations with package size, we show that these cohesion and complexity metrics can be used to identify large package refactoring opportunities. Furthermore, we also discuss why some maintainability metrics (e.g., coupling metrics) may not be useful for refactoring large packages. Editor’s note: Open Science material was validated by the Journal of Systems and Software Open Science Board .

Background: Software smells reflect the sub-optimal patterns in the software. In a similar way, community smells consider the sub-optimal patterns in the organizational and social structures of software teams. Related work performed empirical studies to identify the relationship between community smells and software smells at the architecture and code levels. However, how community smells relate with design smells is still unknown.

Background and Context. Metacognitive skills are important for all students learning to program and interest in applying pedagogical approaches in early programming courses that focus on metacognitive aspects is growing. However, most studies of such approaches are not rigorously based in theory, and when they are, almost always utilize foundational education and psychology theories from as far back as the 1970s. More recent theory is less tested, and not all relevant metacognitive theories have been explored in the computing education research literature.

Abstract Context Tangled commits are changes to software that address multiple concerns at once. For researchers interested in bugs, tangled commits mean that they actually study not only bugs, but also other concerns irrelevant for the study of bugs. Objective We want to improve our understanding of the prevalence of tangling and the types of changes that are tangled within bug fixing commits. Methods We use a crowd sourcing approach for manual labeling to validate which changes contribute to bug fixes for each line in bug fixing commits. Each line is labeled by four participants. If at least three participants agree on the same label, we have consensus. Results We estimate that between 17% and 32% of all changes in bug fixing commits modify the source code to fix the underlying problem. However, when we only consider changes to the production code files this ratio increases to 66% to 87%. We find that about 11% of lines are hard to label leading to active disagreements between participants. Due to confirmed tangling and the uncertainty in our data, we estimate that 3% to 47% of data is noisy without manual untangling, depending on the use case. Conclusion Tangled commits have a high prevalence in bug fixes and can lead to a large amount of noise in the data. Prior research indicates that this noise may alter results. As researchers, we should be skeptics and assume that unvalidated data is likely very noisy, until proven otherwise.

Abstract Clustering algorithms can be optimized using nature‐inspired techniques. Many algorithms inspired by nature, namely, firefly algorithm, ant colony optimization algorithm, and so forth, have improved clustering results. k‐means is a popular clustering technique but has limitations of local optima, which have been overcome using its various hybrids. k‐means++ is a hybrid k‐means clustering algorithm that gives the procedure to initialize centre of the clusters. In the proposed work, hybrids of nature‐inspired techniques using cuckoo and krill herd algorithm are implemented on k‐means++ algorithm to enhance cluster quality and generate optimized clusters. The designed algorithms are implemented, and the results are compared with their counterparts. Performance parameters such as accuracy, f‐measure, error rate, standard deviation, CPU time, cluster quality check, and so forth are used to measure the clustering capabilities of these algorithms. The results indicate the high performance of newly designed algorithms.

Context: Systematic literature reviews and mapping studies usually rely on automated searches of digital libraries to identify primary studies. Defining proper search strings, executing semantically similar searches on different libraries, and reporting limitations of searches increase the reliability of secondary studies. Objective: We aim to survey the current state of using automated searches in secondary software engineering studies. In particular, we aim at analyzing how automated searches are reported and at understanding the reproducibility of secondary studies. Method: We perform a preliminary tertiary study that covers 50 recently published representative secondary studies from different software engineering venues and subfields. Results: We found that most secondary studies complement an automated search with a manual search and use four or more digital libraries. Also, we found that the quality of reporting search strings is rather poor. Finally, we found that most secondary studies do not acknowledge limitations of automated searches and implications of limitations on study findings. Conclusions: Our findings highlight implications for researchers (e.g., to properly report the search process) and for reviewers (e.g., to execute search strings reported in papers). Also, our findings indicate that secondary studies are difficult to replicate.

Affective software engineering deals in exploring and understanding the developers' sentiments associated with specific software development tasks with the goal to unleash various task-to-sentiment relationships. This paper empirically investigates the impact of software code refactoring on the sentiments of developers in open source projects. We perform a comprehensive analysis of sentiments attached with 15 different refactoring activities across the evolution of 60 open source Java projects through mining relevant commit messages. We investigate 3,171 refactoring related commit messages (out of total 615,625 commit messages) representing 4,891 refactoring instances. The study outcome shows that in general software developers express more negative sentiments than positive sentiments, while performing refactoring tasks. It is also found that 5 out of 15 refactoring activities are mainly responsible for this observed outcome.

Case-Based Learning (CBL) is a teaching methodology based on discussing and analyzing real world problems and solutions. A case is like a story, related to a real world situation that sources a number of challenging problems, which have no obvious solutions. There have been various applications of CBL in the fields of Medicine, Law, and Business. However, there are a limited number of evidences related to the application of CBL in the field of Software Engineering (SE). In this paper, we present an open source web application called SEABED (Software Engineering Case-Based Learning Database). The feature set supported by SEABED comprises Case Submission, Case Collection, Case Search, Case Review, and Case Evolution. SEABED aims to develop and evolve a rich repository of SE cases that might become a basis for enabling the students, instructors, practitioners, and experts to enhance their SE knowledge in an effective way. Further, we present our approach to build a vibrant SE case-based learning community that triggers enough activity around SEABED, required for the platform to reach a critical and wider mass. We communicated with several SE educators around the world and received positive responses on SEABED. In order to investigate the effectiveness of the CBL methodology followed by SEABED, we conducted an experimental study at an Institute of National Importance in India. We present the empirical analysis results of this study and explore the impact of CBL on students' learning abilities.

Search phase is considered as one of the most important steps in conducting secondary studies such as systematic literature reviews and mapping studies. In recent times, automatic search in digital libraries and academic search engines has been the preferred method of search phase execution for most software engineering related secondary studies. However, there are no previous studies that report or evaluate the secondary study relevant search features of these electronic data sources. We perform a feature analysis (screening mode) based evaluation of five widely used digital libraries (IEEE Xplore, ACM DL, SpringerLink, ScienceDirect and Wiley) in terms of their respective features required to support the search phase of secondary studies. We identify a total of 68 search related features and conduct a comprehensive exploration into their execution behaviors. The overall work presents a useful caution guide for systematic reviewers who plan to use the identified features for executing various search phase steps of their secondary studies.

Recent research has shown that maintainability improving activities, such as removing code smells using recommended refactoring activities, may degrade the energy consumption behavior of software systems. However, current research is still immature and requires considerable effort for transferring findings to practice. This work empirically investigates the impact of eliminating a set of three notorious code smells, individually as well as in all six possible sequences, on energy consumption behavior of software systems. It also analyzes whether any relationship exists between software architecture sustainability (in terms of energy efficiency) and maintainability within the context of individual and sequential code smell elimination. The study outcomes show that the selected code smell removal permutations yield variant levels of energy consumption values for the resulted refactored software versions. Also, a particular permutation is learned to yield most energy-efficient refactored software versions, when compared to all other code smell removal permutations.

Dynamic metrics are known to assess the actual behavior of software systems as they are extracted from runtime data obtained during program execution. However, recent literature indicates that dealing with dynamic information remains a formidable challenge due to the huge size of execution data at hand, resulting in long processing delays. We present an efficient and scalable technique to extract design level dynamic metrics from Calling Context Tree (CCT) using cloud based MapReduce paradigm. CCT profiles having node count up to 40 million are used to extract a number of dynamic coupling metrics. On an average, 73% increase in performance is observed as compared to sequential analysis. Also other performance characteristics like speed-up and scale-up are analyzed to strengthen the applicability of our parallel computation approach.