Context: The ultimate goal of Continuous Integration (CI) is to support developers in integrating changes into production constantly and quickly through automated build process. While CI provides developers with prompt feedback on several quality dimensions after each change, such frequent and quick changes may in turn compromise software quality without Refactoring. Indeed, recent work emphasized

Context: Several government laws and app markets, such as Google Play, require the disclosure of app data practices to users. These data practices constitute critical privacy requirements statements, since they underpin the app’s functionality while describing how various personal information types are collected, used, and with whom they are shared. Objective: Abstract and ambiguous terminology in

Context: Many organizations develop software systems using open source software (OSS), which is risky due to the high possibility of losing support. Contributors are critical for the survival of OSS projects, but very few new contributors remain with OSS projects to become long-time contributors (LTCs). Identification of factors that contribute to become an LTC can help OSS project owners utilize limited

Context: Programming by Example (PBE) is increasingly assisting human users by recognizing and executing repetitive tasks, such as text editing and spreadsheet manipulation. Yet, existing work falls short on dealing with rich-formatted documents like PowerPoint (PPT) files, when examples are few and collecting them is intrusive. Objective: This article presents TIDY, a PBE-based framework, to assist

Context: Memory error vulnerabilities have been consequential and several well-known, open-source memory error vulnerability detectors exist, built on static and/or dynamic code analysis. Yet there is a lack of assessment of such detectors based on rigorous, quantitative accuracy and efficiency measures while not being limited to specific application domains. Objective: Our study aims to assess and

Context: Systematic Literature Review (SLR), as a tool of Evidence-Based Software Engineering (EBSE), has been widely used in Software Engineering (SE). However, for certain topics in SE, especially those that are trendy or industry driven, academic literature is generally scarce and consequently Gray Literature (GL) becomes a major source of evidence. In recent years, the adoption of Gray Literature

Context Many organizations undertake large-scale projects of application migration due to availability of scalable and cost-efficient technologies. Such legacy application migration projects are very complex since the process involves in-depth profiling of the applications. Objective During the initial profiling phase, it is imperative to understand the underlying complexities of individual applications

Context: Re-architecting monolithic systems with Microservices-based architecture is a common trend. Various companies are migrating to Microservices for different reasons. However, making such an important decision like re-architecting an entire system must be based on real facts and not only on gut feelings. Objective: The goal of this work is to propose an evidence-based decision support framework

Context: Infrastructure-as-code (IaC) is the DevOps tactic of managing and provisioning software infrastructures through machine-readable definition files, rather than manual hardware configuration or interactive configuration tools. Objective: From a maintenance and evolution perspective, the topic has picked the interest of practitioners and academics alike, given the relative scarcity of supporting

Context: Software bug prediction techniques can provide informative guidance in software engineering practices. Over the past 15 years, developer information has been intensively used in bug prediction as features or basic data source to construct other useful models. Objective: Further leverage developer information from a new and straightforward perspective to improve effort-aware bug prediction

Context: New programming languages (e.g., Swift, Go, Rust, etc.) are being introduced to provide a better opportunity for the developers to make software development robust and easy. At the early stage, a programming language is likely to have resource constraints that encourage the developers to seek help frequently from experienced peers active in Question–Answering (QA) sites such as Stack Overflow

The use of structured natural languages to capture requirements provides a reasonable trade-off between ambiguous natural language and unintuitive formal notations. There are two major challenges in making structured natural language amenable to formal analysis: (1) formalizing requirements as formulas that can be processed by analysis tools and (2) ensuring that the formulas conform to the semantics

Context Source code transformation techniques can improve the security of systems against memory exploitation attacks. As such, the chance of exploitation of security vulnerabilities can be decreased by using different controlled source code transformation techniques. In K-variant architecture, multiple variants of a program are generated through a controlled source code transformation to improve the

Context: Cross-Project Defect Prediction (CPDP) aims to leverage the knowledge from label-rich source software projects to promote tasks in a label-poor target software project. Existing CPDP methods have two major flaws. One is that previous CPDP methods only consider global feature representation and ignores local relationship between instances in the same category from different projects, resulting

Context: Information visualization is paramount for the analysis of Big Data. The volume of data requiring interpretation is continuously growing. However, users are usually not experts in information visualization. Thus, defining the visualization that best suits a determined context is a very challenging task for them. Moreover, it is often the case that users do not have a clear idea of what objectives

Context Systematic Literature Review (SLR) studies aim to identify relevant primary papers, extract the required data, analyze, and synthesize results to gain further and broader insight into the investigated domain. Multiple SLR studies have been conducted in several domains, such as software engineering, medicine, and pharmacy. Conducting an SLR is a time-consuming, laborious, and costly effort.

Context: Bug fixing is a frequent and important task in Open Source Software (OSS) development and involves the communication of messages, which can serve for multiple purposes and affect the efficiency and effectiveness of corrective software activities. Objective: This work is aimed at studying the communication functions of bug comments and their associations with fast and complete bug fixing in

Context: Software development involves many activities, and decision making is an essential one. Various factors can impact a decision-making process, and by understanding such factors, one can improve the process. Since people are the ones making decisions, some human-related aspects are amongst those influencing factors. One such aspect is the decision maker’s personality. Objective: This research

Context Organizations often use different applications to support the Scrum process, including project management tools, source repository and quality assessment tools. These applications store useful data for decision-making. However, data items often remain spread in different applications, each of which adopt different data and behavioral models, posing a barrier for integrated data usage. As a

Context: Cognitive load in software engineering refers to the mental effort users spend while reading software artifacts. The cognitive load can vary according to tasks and across developers. Researchers have measured developers’ cognitive load for different purposes, such as understanding its impact on productivity and software quality. Thus, researchers and practitioners can use cognitive load measures

Context: User interface testing validates the correctness of an application through visual cues and interactive events emitted in real-world usages. Performing user interface tests is a time-consuming process, and thus, many studies have focused on prioritizing test cases to help maintain the effectiveness of testing while reducing the need for full execution. Objective: This paper describes a novel

Context: Code Clones have been accepted as a common phenomenon in software, thanks to the increasing demand for rapid production of software. The existence of code clones is recognized by developers in the form of clone group, which includes several pieces of clone fragments that are similar to one another. A change in one of these clone fragments may indicate necessary “consistent changes” are required

Context: Micro-Frontends are increasing in popularity, being adopted by several large companies, such as DAZN, Ikea, Starbucks and may others. Micro-Frontends enable splitting of monolithic frontends into independent and smaller micro applications. However, many companies are still hesitant to adopt Micro-Frontends, due to the lack of knowledge concerning their benefits. Additionally, provided online

Context: Software architecture refactoring can be induced by multiple reasons, such as satisfying new functional requirements or improving non-functional properties. Multi-objective optimization approaches have been widely used in the last few years to introduce automation in the refactoring process, and they have revealed their potential especially when quantifiable attributes are targeted. However

Context The tremendous growth of Android malware in recent years is a strong motivation for the vast endeavor in detection and analysis of malware apps. A prominent approach for this purpose is dynamic analysis in which providing complex interactions with the samples under analysis is a need. Event generation tools are almost used to provide such interactions, but they have deficiencies for effective

Context: The precision and cost of static analysis are determined by abstraction heuristics (e.g., strategies for abstracting calling contexts, heap locations, etc.), but manually designing effective abstraction heuristics requires a huge amount of engineering effort and domain knowledge. Recently, data-driven static analysis has emerged to address this challenge by learning such heuristics automatically

Context: Previous studies have shown that existing deep learning-based approaches can significantly improve the performance of vulnerability detection. They represent code in various forms and mine vulnerability features with deep learning models. However, the differences of code representation forms and deep learning models make various approaches still have some limitations. In practice, their false-positive

Context : As part of the arms race, one emerging attack methodology has been control-hijacking attacks, e.g., return-oriented programming (ROP). Control-flow integrity (CFI) is a generic and effective defence against most control-hijacking attacks. However, existing CFI mechanisms have poor security as demonstrated by their equivalence class (EC) sizes, which are sets of targets that CFI policies cannot

Context: Agent-based models play an important role in simulating complex emergent phenomena and supporting critical decisions. In this context, a software fault may result in poorly informed decisions that lead to disastrous consequences. The ability to rigorously test these models is therefore essential. Objective: Our objective is to summarise the state-of-the-art techniques for test case generation

Context: Analyzing software maintenance activities is very helpful in ensuring cost-effective evolution and development activities. The categorization of commits into maintenance tasks supports practitioners in making decisions about resource allocation and managing technical debt. Objective: In this paper, we propose to use a pre-trained language neural model, namely BERT (Bidirectional Encoder Representations

Context: Feasible test sequences generation is a key step in protocol conformance testing based on the Extended Finite State Machine (EFSM) model. To guarantee the feasibility of generated test sequences, transition executability analysis (TEA) technique is widely applied in automatic test derivation. However, the TEA method often suffers from the famous state explosion problem, which has become a

Context Extracting conceptual models, e.g., entity relationship model or Business Process model, from software requirement document is an essential task in the software development life cycle. Business process model presents a clear picture of required system's functionality. Operations in business process model together with the data entity consumed, help the software developers to understand the

Context: Industry 4.0 has substantially changed the manufacturing processes, leading to smart factories with full digitalization, intelligence, and dynamic production. The need for rigorous and continuous development of highly networked software-intensive Industry 4.0 systems entails great challenges. Hence, Industry 4.0 requires new ways to develop, operate, and evolve these systems accordingly. Objective:

Context: With the prevalence of publicly available source code repositories to train deep neural network models, neural program models can do well in source code analysis tasks such as predicting method names in given programs that cannot be easily done by traditional program analysis techniques. Although such neural program models have been tested on various existing datasets, the extent to which

Context It is essential to be aware of the energy efficiency of software when it is running, so that it can be improved; to that end, energy consumption measurements need to be carried out. To ensure that these measurements are as reliable as possible, it is recommended that a well-defined process be followed. Objective To identify how the process for analysing the energy efficiency of software should

Context To ensure the success of software projects, it is essential to select skilled developers and to use suitable work contracts. Objective This study tests two hypotheses: (i) the use of work-sample testing (trialsourcing) improves the selection of skilled software developers; and (ii) the use of contracts based on hourly payment leads to better software project outcomes than fixed-price contracts

Context: Continuous experimentation and A/B testing is an established industry practice that has been researched for more than 10 years. Our aim is to synthesize the conducted research. Objective: We wanted to find the core constituents of a framework for continuous experimentation and the solutions that are applied within the field. Finally, we were interested in the challenges and benefits reported

Context: An evidence-based scientific discipline should produce, consume and disseminate credible evidence. Unfortunately, mistakes are sometimes made, resulting in the production, consumption and dissemination of invalid or otherwise questionable evidence. In the worst cases, such questionable evidence achieves the status of accepted knowledge. There is, therefore, the need to ensure that producers

Context: Code and design smells, such as the coupling smells examined in this article, are widely studied. Existing empirical studies reveal gaps between the scientific theory and practice, not yet explained by the scientific literature. Only basic coupling smell detection approaches and metrics seem to have been transferred to practice so far. Objective: This article aims to study the current practitioner’s

Context: Automatic fault localization is essential to intelligent software system. Most fault localization techniques assume the test oracle is perfect before debugging, which is hard to exist in practice. In fact, the test suite would contain a number of unlabelled test cases which have been proved to be useful in fault localization. However, due to the execution diversity, not all unlabelled test

Context: Developers tend to search and reuse code snippets from a large-scale codebase when they want to implement some functions that exist in the previous projects, which can enhance the efficiency of software development. Objective: As the first deep learning-based code search model, DeepCS outperforms prior models such as Sourcere and CodeHow. However, it utilizes two separate LSTM to represent

Context Enterprise architecture (EA) is a collection of artifacts describing various aspects of an organization from an integrated business and IT perspective. EA practice is an organizational activity that implies using EA artifacts for facilitating decision-making and improving business and IT alignment. EA practice involves numerous participants ranging from C-level executives to project teams and

Context In large software projects bug fixing is a time-bound, time-consuming, mind-numbing, and challenging task that requires a collaborative effort with multiple developers, separated geographically. Objective The objective of this paper is to propose a decentralized automated bug assignment approach to improve the quality of bug assignments to minimize backlogs and overall bug fixing time. Method

Context: Given its competitiveness, the video-game industry has a closed-source culture. Hence, little is known about the problems faced by game developers. However, game developers do share information about their game projects through postmortems, which describe informally what happened during the projects. Objective: The software-engineering research community and game developers would benefit from

Context: Requirements engineering and software architecture are tightly linked disciplines. The Twin Peaks model suggests that requirements and architectural components should stay aligned while the system is designed and as the level of detail increases. Unfortunately, this is hardly the case in practical settings. Objective: We surmise that a reason for the absence of conjoint evolution is that existing

Context: Agile software development methods advocate the importance of producing working software without comprehensive documentation. While this approach seems to suit the evolutionary nature of realistic software development for many applications, even including safety-critical systems, it faces two major challenges. One is the lack of a comprehensible specification for code evolution and future

Context: Modern systems require programmers to develop code that dynamically adapts to different contexts, leading to the evolution of new context-oriented programming languages. These languages introduce new software-engineering challenges, such as: how to maintain the separation of concerns of the codebase? how to model the changing behaviors? how to verify the system behavior? and more. Objective:

Context Pricing is an essential element of software business strategy and tactics. Informed pricing decision-making requires the involvement of different stakeholders and comprehensive data analysis. Achieving both appears to be challenging, and pricing remains one of the most under-managed processes in the software business. Simultaneously, a coherent SaaS pricing body of knowledge and verified solutions

Context: Bug reports record issues found during software development and maintenance. A high-impact bug report (HBR) describes an issue that can cause severe damage once occurred after deployment. Identifying HBRs from the bug repository as early as possible is crucial for guaranteeing software quality. Objective: In recent years, many machine learning-based approaches have been proposed for HBR prediction

Background: Case studies are regularly published in the software engineering literature, and guidelines for conducting case studies are available. Based on a perception that the label “case study” is assigned to studies that are not case studies, an investigation has been conducted. Objective: The aim was to investigate whether or not the label “case study” is correctly used in software engineering

Context: In the field of software engineering, the most complex and time consuming activity is fault-finding. Due to increasing size and complexity of software, there is a necessity of automated fault detection tool which can detect fault with minimal human intervention. A programmer spends a lot of time and effort on software fault localization. Various Spectrum Based Fault Localization (SBFL) techniques

Background: Internet of Things (IoT) enables the interaction among objects to provide services to their users. Areas such as eHealth, smart energy, and smart buildings have been benefiting from the IoT potential. However, the development of IoT systems is still complex because it deals with a highly dynamic, volatile, and heterogeneous environment. These characteristics require discovering devices

Context: Software evolution and maintenance activities in today’s Free/Libre Open Source Software (FLOSS) rely primarily on information extracted from bug reports registered in bug tracking systems. Many studies point out that most bugs that adversely affect the user’s experience across versions of FLOSS projects are long-lived bugs. However, proposed approaches that support bug fixing procedures do

Context: Previous studies proposed different kinds of approaches for class integration test order generation, and corresponding systems can be implemented based on these approaches. Such class integration test order generation systems can facilitate the process of software integration testing if they are implemented correctly. Objective: However, a test oracle problem exists in the class integration

Context: Mutual Information is an information theoretic measure designed to quantify the amount of similarity between two random variables ranging over two sets. In this paper, we adapt this concept and show how it can be used to select a good test suite to test from a Finite State Machine (FSM) based on a maximise diversity approach. Objective: The main goal of this paper is to use Mutual Information

Context: Mutation testing techniques, which attempt to construct a set of so-called mutants by seeding various faults into the software under test, have been widely used to generate test cases as well as to evaluate the effectiveness of a test suite. Its popularity in practice is significantly hindered by its high cost, majorly caused by the large number of mutants generated by the technique. Objective:

Context: Mutation analysis is a popular but costly approach to assess the quality of test suites. One recent promising direction in reducing costs of mutation analysis is to identify redundant mutations, i.e., mutations that are subsumed by some other mutations. A previous approach found redundant mutants manually through truth tables but it cannot be applied to all mutations. Another work derives

Context Requirement prioritisation (RP) is often used to select the most important system requirements as perceived by system stakeholders. RP plays a vital role in ensuring the development of a quality system with defined constraints. However, a closer look at existing RP techniques reveals that these techniques suffer from some key challenges, such as scalability, lack of quantification, insufficient

Context Risk event management has become strategic in Project Management, where uncertainties are inevitable. In this sense, the use of concepts of ubiquitous computing, such as contexts, context histories, and mobile computing can assist in proactive project management. Objective This paper proposes a computational model for the reduction of the probability of project failure through the prediction