Code commenting is a common programming practice of practical importance to help developers review and comprehend source code. There are two main types of code comments for a method: header comments that summarize the method functionality located before a method, and block comments that describe the functionality of the code snippets within a method. Inspired by the effectiveness of deep learning techniques

Context: The development of software systems is a complex activity because of its nature and the management of its construction. It is challenging to create and follow a plan. Moreover, budget overrun is a common consequence of this situation. Agile methods, like Scrum, help to mitigate this problem using incremental and iterative development. Agile methods jump start new developments, but it is difficult

Context:Kotlin is a new programming language representing an alternative to Java; they both target the same JVM and can safely coexist in the same application. Kotlin is advertised as capable to solve several known limitations of Java. Recent surveys show that Kotlin achieved a relevant diffusion among Java developers. Goal:We planned to empirically assess a few typical promises of Kotlin w.r.t. known

Context Modern software systems employ large IT infrastructures hosted in on-premise clouds or using “rented” cloud resources from specific vendors. The unifying force across any cloud strategy is incremental product and application improvement against conservation of those resources. This is where monitoring of cloud applications becomes a key asset Objective To shed light over the status of monitoring

Context: A Software Product Line (SPL) can express the variability of a system through the specification of configuration options. Evaluating performance characteristics, such as the system response time and resource utilization, of a software product is challenging, even more so in the presence of uncertain values of the attributes. Objective: The goal of this paper is to automate the generation of

Context – During the development of complex software systems, programmers look for external resources to understand better how to use specific APIs and to get advice related to their current tasks. Stack Overflow provides developers with a broader insight into API usage as well as useful code examples. Given the circumstances, tools and techniques for mining Stack Overflow are highly desirable. Objective

Context: Search-Based Software Engineering (SBSE) is the research field where Software Engineering (SE) problems are modelled as search problems to be solved by search-based techniques. The Symposium on Search Based Software Engineering (SSBSE) is the premier event on SBSE, which had its 11th edition in 2019. Objective: In order to better understand the characteristics and evolution of papers published

Background: Developing and maintaining large scale machine learning (ML) based software systems in an industrial setting is challenging. There are no well-established development guidelines, but the literature contains reports on how companies develop and maintain deployed ML-based software systems. Objective: This study aims to survey the literature related to development and maintenance of large

Context Microservices must be composed to provide users with complex and elaborated functionalities. It seems that the decentralized nature of microservices makes a choreography style more appropriate to achieve such cooperation, where lighter solutions based on asynchronous events are generally used. However, a microservice composition based on choreography distributes the flow logic of the composition

Context Program slicing is very useful in program analysis and software engineering. It computes the slice, which is a part of program and contains all the statements related to the given slicing criterion. The more accurate a slicing technique could be, the smaller the slice is. Objective This paper aims to improve the current slicing accuracy for object-oriented programs. The slicing accuracy is

Context With the ever-increasing evolution of software systems, their architecture is subject to frequent changes due to multiple reasons, such as new requirements. Appropriate architectural changes driven by non-functional requirements are particularly challenging to identify because they concern quantitative analyses that are usually carried out with specific languages and tools. A considerable number

Context Systematic Literature Reviews (SLRs) have been adopted within Software Engineering (SE) for more than a decade to provide meaningful summaries of evidence on several topics. Many of these SLRs are now potentially not fully up-to-date, and there are no standard proposals on how to update SLRs in SE. Objective The objective of this paper is to propose guidelines on how to best search for evidence

Context Crowdsourced software testing (CST) refers to the use of crowdsourcing techniques in the domain of software testing. CST is an emerging area with its applications rapidly increasing in the last decade. Objective A comprehensive review on CST has been conducted to determine the current studies aiming to improve and assess the value of using CST as well as the challenges identified by these evaluation

Context Code reuse, generally done through software packages, allows developers to reduce time-to-market and improve code quality. The npm ecosystem is a Node.js package management system which contains more than 700 K Node.js packages and to help developers find high-quality packages that meet their needs, npms developed a search engine to rank Node.js packages in terms of quality, popularity, and

Context Businesses today must collaborate in a coordinated fashion. To collaborate, they must align their business processes and IT by complying to a common reference architecture. The common reference architecture that addresses their specific collaboration requirements is generally an adaptation of an existing generic reference architecture. However, a design framework for adapting reference architectures

Context Software defect prediction is an important technique that can help practitioners allocate their quality assurance efforts. In recent years, just-in-time (JIT) defect prediction has attracted considerable interest, as it enables developers to identify risky changes at check-in time. Objective Many studies have conducted research from supervised and unsupervised perspectives. A model that does

Context There are several methods to elicit requirements through interviews between an end-user and a team of software developers. The choice of the best method in this context is usually on subjective developers’ preferences instead of objective reasons. There is a lack of empirical evaluations of methods to elicit requirements that help developers to choose the most suitable one. Objective This paper

Context Previous research found that the performance of a team not only depends on the team personality composition, but also on the interactive effects of team climate. Although investigation on personalities associated with software development has been an active research area over the past decades, there has been very limited research in relation to team climate. Objective Our study investigates

Context There are many duplicate bug reports in the semi-structured software repository of various software bug triage systems. The duplicate bug report detection (DBRD) process is a significant problem in software triage systems. Objective The DBRD problem has many issues, such as efficient feature extraction to calculate similarities between bug reports accurately, building a high-performance duplicate

Context Critical systems in application domains such as automotive, railway, aerospace, and healthcare are required to comply with safety standards. The understanding of the safety compliance needs specified in these standards can be difficult from their text. A possible solution is to use models. Objective We aim to evaluate the use of models to understand safety compliance needs. Method We have studied

Context In host-based anomaly detection, feature extraction on the system call traces is important to build an effective anomaly detection model. Different kinds of feature extraction methods are recently proposed and most of them aim at preserving the positional information of the system calls within a trace. These extracted features are generally named from system calls, therefore, cannot be used

Context Recent advancements in the applicability of neural networks across a variety of fields, such as computer vision, natural language processing and others, have re-sparked an interest in program induction methods. (Kitzelman [1], Gulwani et al. [2] or Kant [3].) Problem When performing a program induction task, it is not feasible to search across all possible programs that map an input to an output

Context Software development incorporates numerous people with diverse expertise and expectations. This makes conflict a common phenomenon in software development. Besides human causes, many conflicts in software development root in the tools and processes. Moreover, the growing role of software in any type of system is increasing the heterogeneity in software projects. The number and variety of tools

Context Code refactoring was conceived for enhancing code structures, often in terms of internal quality attributes such as cohesion and coupling. Developers may have to apply multiple refactoring operations to achieve the expected enhancement. Re-refactoring occurs whenever one or more refactoring operations are performed on a previously refactored code element. The literature often assumes each single

Context: Cyber-Physical Systems (CPSs) are gradually and widely introducing autonomous capabilities into everything. However, human participation is required to accomplish tasks that are better performed with humans (often called human-in-the-loop). In this way, human-in-the-loop solutions have the potential to handle complex tasks in unstructured environments, by combining the cognitive skills of

Context: With the current high trends of deploying and using web services in practice, effective techniques for maintaining high quality of Service are becoming critical for both service providers and subscribers/users. Service providers want to predict the quality of service during early stages of development before releasing them to customers. Service clients consider the quality of service when

Context Maintenance activities cannot be completed without locating the set of software artifacts that realize a particular feature of a software system. Manual Feature Location (FL) is widely used in industry, but it becomes challenging (time-consuming and error prone) in large software repositories. To reduce manual efforts, automated FL techniques have been proposed. Research efforts in FL tend

Context The purpose of software restructuring, or refactoring, is to improve software quality and developer productivity. Objective Prior studies have relied mainly on static and dynamic analysis of code to detect and recommend refactoring opportunities, such as code smells. Once identified, these smells are fixed by applying refactorings which then improve a set of quality metrics. While this approach

Context Millions of software developers are using open-source software (OSS) platforms to host their code and collaborate with each other. They possess different programming skills, styles, and preferences, etc., and it is important to understand them for making collaborative decisions such as programming task assignment. Existing OSS platforms do not provide sufficient information about developers

Context To reduce manual effort of extracting test cases from natural-language requirements, many approaches based on Natural Language Processing (NLP) have been proposed in the literature. Given the large amount of approaches in this area, and since many practitioners are eager to utilize such techniques, it is important to synthesize and provide an overview of the state-of-the-art in this area. Objective

Context An information retrieval-based bug localization (IRBL) method is proposed to localize buggy files using a bug report as a query. The performance of this method strongly depends on the quality of the query. However, these queries contain noise terms that hinder their use for IRBL. To improve the quality of a query, an automatic query reduction (AQR) technique that removes noise words from the

Context Combinatorial interaction testing is known to be an efficient testing strategy for computing and information systems. Locating arrays are mathematical objects that are useful for this testing strategy, as they can be used as a test suite that permits fault localization as well as fault detection. In this application, each row of an array is used as an individual test. Objective This paper proposes

Context Model Driven Engineering (MDE) aims to alleviate complexity and improve reusability in software development. The development of complex software implies to divide it into independent parts before then assembled. This is how the problem of model composition has become an interesting and stills an emerging topic in MDE. Objective Our goal is to analyze the current state of the art in model composition

Context: In this paper, we investigate how developers discuss code smells and anti-patterns across three technical Stack Exchange sites. Understanding developers perceptions of these issues is important to inform and align future research efforts and direct tools vendors to design tailored tools that best suit developers. Method: we mined three Stack Exchange sites and used quantitative and qualitative

Context Software similarity comparison has always been a common technique for software reuse detection, plagiarism detection, and defect detection. Objective Considering the role of API calls and arithmetic operations in software execution, a semantic-based dynamic software analysis method–mixed key instruction sequence (MKIS) is proposed. Method MKIS embeds key value sets into a vector and constructs

Context: An analogy-based software effort estimation technique estimates the required effort for a new software project based on the total effort used in completing past similar projects. In practice, offering high accuracy can be difficult for the technique when the new software project is not similar to any completed projects. In this case, the accuracy will rely heavily on a process called effort

Context Mutation testing is considered to be a powerful approach to assess and improve the quality of test suites. However, this technique is expensive mainly because some mutants are semantically equivalent to the original program; in general, equivalent mutants require manual revision to differentiate them from useful ones, which is known as the Equivalent Mutant Problem (EMP). Objective In the past

Context: Recently deep learning based Natural Language Processing (NLP) models have shown great potential in the modeling of source code. However, a major limitation of these approaches is that they take source code as simple tokens of text and ignore its contextual, syntactical and structural dependencies. Objective: In this work, we present CodeGRU, a gated recurrent unit based source code language

Context: Genetic algorithms can be utilized for automatic test data generation. Test data are encoded as individuals which are evolved for a number of generations using genetic operators. Test data of a parallel program include not only the program input, but also the communication information between each pair of processes. Traditional genetic algorithms, however, do not make full use of information

Context: Security bug reports (SBRs) usually contain security-related vulnerabilities in software products, which could be exploited by malicious attackers. Hence, it is important to identify SBRs quickly and accurately among bug reports (BRs) that have been disclosed in bug tracking systems. Although a few methods have been already proposed for the detection of SBRs, challenging issues still remain

Context Multiple fault localization (MFL) is the act of identifying the locations of multiple faults (more than one fault) in a faulty software program. This is known to be more complicated, tedious, and costly in comparison to the traditional practice of presuming that a software contains a single fault. Due to the increasing interest in MFL by the research community, a broad spectrum of MFL debugging

Context Internet of Things (IoT) is a promising paradigm due to the growing number of devices that may be connected, defined as “things”. Managing these “things” is still considered a challenge. One way to overcome this challenge may be by adopting the software product line (SPL) paradigm and the variability management (VM) activity. SPL engineering consists of mechanisms that provide identification

Context Quality requirements (QRs) describe the desired quality of software, and they play an important role in the success of software projects. In agile software development (ASD), QRs are often ill-defined and not well addressed due to the focus on quickly delivering functionality. Rapid software development (RSD) approaches (e.g., continuous delivery and continuous deployment), which shorten delivery

Context The importance of automotive software has been rapidly increasing because software controls many components of motor vehicles such as smart-key system, tire pressure monitoring system, and advanced driver assistance system. Consequently, the automotive industry spends a large amount of human effort to test automotive software and is interested in automated testing techniques to ensure high-quality

Context The severity of a bug is often used as an indicator of how a bug negatively affects system functionality. It is used by developers to prioritize bugs which need to be fixed. The problem is that, for various reasons, bug submitters often enter the incorrect severity level, delaying the bug resolution process. Techniques that can automatically predict the severity of a bug can significantly reduce

Context: Neural Network (NN) algorithms have been successfully adopted in a number of Safety-Critical Cyber-Physical Systems (SCCPSs). Testing and Verification (T&V) of NN-based control software in safety-critical domains are gaining interest and attention from both software engineering and safety engineering researchers and practitioners. Objective: With the increase in studies on the T&V of NN-based

Context:Behavior-Driven Development (BDD) features the capability, through appropriate domain-specific languages, of specifying acceptance test cases and making them executable. The availability of frameworks such as Cucumber or RSpec makes the application of BDD possible in practice. However, it is unclear to what extent developers use such frameworks, and whether they use them for actually performing

Context : Software testing is a complex area since it has a large number of specific methods, processes and strategies, involving a lot of domain concepts. Therefore, it would be valuable to have a conceptualized software testing ontology that explicitly and unambiguously defines the concepts. Consequently, it is important to find out the available evidence in the literature on primary studies for

Context : Nowadays, the use of GPU to improve performance of computationally expensive systems are widely explored. On GPU-accelerated programs, performance is related to the partition of the problem into blocks of threads in such a way that the parallel tasks to be executed better fit the GPU architecture. Although there exists some general guidelines to help defining block dimensions, finding the

Context:Software testing based on finite-state machines. Objective:Improving the performance of existing testing methods by construction of more efficient separating sequences, so that states entered by a system under test can be identified in a much shorter span of time. Method: This paper proposes an efficient way to construct separating sequences for subsets of states for any deterministic finite-state

Context: Functional Size Measurement (FSM) methods, like Function Points Analysis (FPA) or COSMIC, are well-established approaches to estimate software size. Several approximations of these methods have been recently proposed as they require less time/information to be applied, however their effectiveness for effort prediction is not known. Objective: The effectiveness of approximated functional size

Context: The quality of user experience is the cornerstone of any organization’s successful digital transformation journey. Web pages are the main touchpoint for users to access services in a digital mode. Web page performance is a key determinant of the quality of user experience. The negative impact of poor web page performance on the productivity, profits, and brand value of an organization is well-recognized

Context When conducting a Systematic Literature Review (SLR), researchers usually face the challenge of designing a search strategy that appropriately balances result quality and review effort. Using digital library (or database) searches or snowballing alone may not be enough to achieve high-quality results. On the other hand, using both digital library searches and snowballing together may increase

Context The model-based analysis is preferred over the code-based analysis as it speeds up the development process and directs the guiding effort. In the software industry, the Unified Modeling Language (UML) is a set standard followed by the developers as well as system analysts to extract all attainable paths of controls, usually known as scenarios under an activity diagram. Objective In this manuscript

Context: Software Fault Localisation (FL) refers to finding faulty software elements related to failures produced as a result of test case execution. This is a laborious and time consuming task. To allow FL automation search-based algorithms have been successfully applied in the field of Search-Based Fault Localisation (SBFL). However, there is no study mapping the SBFL field to the best of our knowledge

Context : Software vulnerability detection is essential to ensure cybersecurity. Currently, most software is published in binary form, thus researchers can only detect vulnerabilities in these software by analysing binary programs. Although existing research approaches have made a substantial contribution to binary vulnerability detection, there are still many deficiencies, such as high false positive

CONTEXT Scientific software plays an important role in critical decision making, for example making weather predictions based on climate models, and computation of evidence for research publications. Recently, scientists have had to retract publications due to errors caused by software faults. Systematic testing can identify such faults in code. OBJECTIVE This study aims to identify specific challenges

CONTEXT A distributed business process is executed in a distributed computing environment. The service-oriented architecture (SOA) paradigm is a popular option for the integration of software services and execution of distributed business processes. Entailment constraints, such as mutual exclusion and binding constraints, are important means to control process execution. Mutually exclusive tasks result