Software Engineering Challenges Research Articles

Predictors of well-being and productivity among software professionals during the COVID-19 pandemic - a longitudinal study.

The COVID-19 pandemic has forced governments worldwide to impose movement restrictions on their citizens. Although critical to reducing the virus’ reproduction rate, these restrictions come with far-reaching social and economic consequences. In this paper, we investigate the impact of these restrictions on an individual level among software engineers who were working from home. Although software professionals are accustomed to working with digital tools, but not all of them remotely, in their day-to-day work, the abrupt and enforced work-from-home context has resulted in an unprecedented scenario for the software engineering community. In a two-wave longitudinal study (N = 192), we covered over 50 psychological, social, situational, and physiological factors that have previously been associated with well-being or productivity. Examples include anxiety, distractions, coping strategies, psychological and physical needs, office set-up, stress, and work motivation. This design allowed us to identify the variables that explained unique variance in well-being and productivity. Results include (1) the quality of social contacts predicted positively, and stress predicted an individual’s well-being negatively when controlling for other variables consistently across both waves; (2) boredom and distractions predicted productivity negatively; (3) productivity was less strongly associated with all predictor variables at time two compared to time one, suggesting that software engineers adapted to the lockdown situation over time; and (4) longitudinal analyses did not provide evidence that any predictor variable causal explained variance in well-being and productivity. Overall, we conclude that working from home was per se not a significant challenge for software engineers. Finally, our study can assess the effectiveness of current work-from-home and general well-being and productivity support guidelines and provides tailored insights for software professionals.

Embedding API dependency graph for neural code generation

The problem of code generation from textual program descriptions has long been viewed as a grand challenge in software engineering. In recent years, many deep learning based approaches have been proposed, which can generate a sequence of code from a sequence of textual program description. However, the existing approaches ignore the global relationships among API methods, which are important for understanding the usage of APIs. In this paper, we propose to model the dependencies among API methods as an API dependency graph (ADG) and incorporate the graph embedding into a sequence-to-sequence (Seq2Seq) model. In addition to the existing encoder-decoder structure, a new module named ``embedder" is introduced. In this way, the decoder can utilize both global structural dependencies and textual program description to predict the target code. We conduct extensive code generation experiments on three public datasets and in two programming languages (Python and Java). Our proposed approach, called ADG-Seq2Seq, yields significant improvements over existing state-of-the-art methods and maintains its performance as the length of the target code increases. Extensive ablation tests show that the proposed ADG embedding is effective and outperforms the baselines.

Reparation in Evolutionary Algorithms for Multi-objective Feature Selection in Large Software Product Lines

Software Product Lines Engineering is the area of software engineering that aims to systematise the modelling, creation and improvement of groups of interconnected software systems by formally expressing possible alternative products in the form of Feature Models. Deriving a software product/system from a feature model is called Feature Configuration. Engineers select the subset of features (software components) from a feature model that suits their needs, while respecting the underlying relationships/constraints of the system–which is challenging on its own. Since there exist several (and often antagonistic) perspectives on which the quality of software could be assessed, the problem is even more challenging as it becomes a multi-objective optimisation problem. Current multi-objective feature selection in software product line approaches (e.g., SATIBEA) combine the scalability of a genetic algorithm (IBEA) with a solution reparation approach based on a SAT solver or one of its derivatives. In this paper, we propose MILPIBEA, a novel hybrid algorithm which combines IBEA with the accuracy of a mixed-integer linear programming (MILP) reparation. We show that the MILP reparation modifies fewer features from the original infeasible solutions than the SAT reparation and in a shorter time. We also demonstrate that MILPIBEA outperforms SATIBEA on average on various multi-objective performance metrics, especially on the largest feature models. The other major challenge in software engineering in general and in software product lines, in particular, is evolution. While the change in software components is common in the software engineering industry, the particular case of multi-objective optimisation of evolving software product lines is not well-tackled yet. We show that MILPIBEA is not only able to better take advantage of the evolution than SATIBEA, but it is also the one that continues to improve the quality of the solutions when SATIBEA stagnates. Overall, IBEA performs better when combined with MILP instead of SAT reparation when optimising the multi-objective feature selection in large and evolving software product lines.

Designing and Developing e-Assessment Delivery System Under IMS QTI ver.2.2 Specification

Using technology to create online examination is important, more than that, exploring the recent research in the area of standardization of the learning and the assessment content is of paramount. However, there is still a lack of reusable, interoperable and exchangeable learning resources that afford much motivation to instructors. Assessing acquired knowledge is the primary task of any Learning Management Systems (LMS). Consequent-ly, sharing evaluation contents is a major challenge for software engineering. In this paper, we present the “CleverTesting” assessment authoring tool, focusing on its architecture and its conformance to the Question and Test Interoperability (QTI) specification proposed by IMS Global Learning Consortium. We adopt the QTI specification to create reusable, interoperable and sharable assessment content. Our system has three important components: The first component is “CleverTesting” a quiz authoring tool that allows building sharable question items. The second represents a question bank. The latest component is a player for interpreting and parsing QTI XML documents.

Context-Oriented Behavioral Programming

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 and keep the separation of concerns of the codebase? how to model the changing behaviors? how to verify the system behavior? and more. This paper introduces Context-Oriented Behavioral Programming(COBP) - a novel paradigm for developing context-aware systems, centered on natural and incremental specification of context-dependent behaviors. As the name suggests, we combine behavioral-programming(BP) - a scenario-based modeling paradigm - with context idioms that explicitly specify when scenarios are relevant and what information they need. The core idea is to connect the behavioral model with a data model that represents the context, allowing an intuitive connection between the models via update and select queries. Combining behavioral-programming with context-oriented programming brings the best of the two worlds, solving issues that arise when using each of the approaches in separation. We begin with providing abstract semantics for COBP, laying the foundations for applying reasoning algorithms to context-aware behavioral programs. We then exemplify the semantics with formal specifications of systems, including a variant of Conway's Game of Life. Finally, we present a JavaScript-based implementation of the paradigm and provide two case studies of real-life context-aware systems (one in robotics and another in IoT) that were developed using this tool. Throughout the examples and case studies, we provide design patterns and a methodology for coping with the above challenges.

ACCORDANT: A domain specific-model and DevOps approach for big data analytics architectures

Big data analytics (BDA) applications use machine learning algorithms to extract valuable insights from large, fast, and heterogeneous data sources. New software engineering challenges for BDA applications include ensuring performance levels of data-driven algorithms even in the presence of large data volume, velocity, and variety (3Vs). BDA software complexity frequently leads to delayed deployments, longer development cycles, and challenging performance assessment. This paper proposes a Domain-Specific Model (DSM), and DevOps practices to design, deploy, and monitor performance metrics in BDA applications. Our proposal includes a design process, and a framework to define architectural inputs, software components, and deployment strategies through integrated high-level abstractions to enable QS monitoring. We evaluate our approach with four use cases from different domains to demonstrate a high level of generalization. Our results show a shorter deployment and monitoring times, and a higher gain factor per iteration compared to similar approaches.

How we refactor and how we document it? On the use of supervised machine learning algorithms to classify refactoring documentation

Refactoring is the art of improving the structural design of a software system without altering its external behavior. Today, refactoring has become a well-established and disciplined software engineering practice that has attracted a significant amount of research presuming that refactoring is primarily motivated by the need to improve system structures. However, recent studies have shown that developers may incorporate refactoring strategies in other development-related activities that go beyond improving the design especially with the emerging challenges in contemporary software engineering. Unfortunately, these studies are limited to developer interviews and a reduced set of projects.To cope with the above-mentioned limitations, we aim to better understand what motivates developers to apply a refactoring by mining and automatically classifying a large set of 111,884 commits containing refactoring activities, extracted from 800 open source Java projects. We trained a multi-class classifier to categorize these commits into three categories, namely, Internal Quality Attribute, External Quality Attribute, and Code Smell Resolution, along with the traditional Bug Fix and Functional categories. This classification challenges the original definition of refactoring, being exclusive to improving software design and fixing code smells. Furthermore, to better understand our classification results, we qualitatively analyzed commit messages to extract textual patterns that developers regularly use to describe their refactoring activities.The results of our empirical investigation show that (1) fixing code smells is not the main driver for developers to refactoring their code bases. Refactoring is solicited for a wide variety of reasons, going beyond its traditional definition; (2) the distribution of refactoring operations differs between production and test files; (3) developers use a variety of patterns to purposefully target refactoring-related activities; (4) the textual patterns, extracted from commit messages, provide better coverage for how developers document their refactorings.

Vehicle Software Engineering (VSE): Research and Practice

The Internet of Things (IoT) is shaping the future of the automotive industry. Grounded on the advances in everything from sensors, electronic controllers, artificial intelligence, data analytics, to network connectivity, intelligent connected autonomous vehicles (CAVs) have become the essence in IoT applications. The software in CAVs lies at the core of this digital transformation. Faulty software remains the main reason behind the vast number of safety recalls and reputation damage witnessed recently in the automotive industry. The uniqueness of CAVs originates challenges for vehicle software engineering (VSE) that render traditional models and practical solutions for software development ineffective and inapplicable. Despite the raised necessity to adopt a software engineering model that can handle these challenges, there is a lack of studies recognizing the importance of VSE. This article presents an in-depth and comprehensive analysis to perceive the existing software engineering processes detailing their strengths and limitations in the context of CAVs. It also reviews current practical software solutions, including standards, tools, languages, and research efforts to understand the evolution, trends, and current practice in this article area. This article will enable automakers and software providers to better assess and differentiate among the existing software engineering processes and current practical solutions for vehicle software system development. Hence, they would be able to adopt a VSE model and follow best practices that can better meet their challenging needs.

Trends and Challenges for Software Engineering in the Mobile Domain

Mobile computing is becoming a key aspect of our lives. This article builds on conversations held during the 2018 IEEE/ACM International Conference on Mobile Software Engineering and Systems, highlighting mobile computing's impact on software engineering practices, the transition from research to industry, and related areas.

Fret: Functional Reinforced Transformer With BERT for Code Summarization

Code summarization has long been viewed as a challenge in software engineering because of the difficulties of understanding source code and generating natural language. Some mainstream methods combine abstract syntax trees with language models to capture the structural information of the source code and generate relatively satisfactory comments. However, these methods are still deficient in code understanding and limited by the long dependency problem. In this paper, we propose a novel model called Fret, which stands for Functional REinforced Transformer with BERT. The model provides a new way to generate code comments by learning code functionalities and deepening code understanding while alleviating the problem of long dependency. For this purpose, a novel reinforcer is proposed for learning the functional contents of code so that more accurate summaries to describe the code functionalities can be generated. In addition, a more efficient algorithm is newly designed to capture the source code structure. The experimental results show that the effectiveness of our model is remarkable. Fret significantly outperforms all the state-of-the-art methods we examine. It pushes the BLEU-4 score to 24.32 for Java code summarization (14.23% absolute improvement) and the ROUGE-L score to 40.12 for Python. An ablation test is also conducted to further explore the impact of each component of our method.

Minimal Specifications for Detecting Security Vulnerabilities

Computers are nearly ubiquitous in modern society with uses from maintaining friendships and monitoring homes to managing money and coordinating health care. As the roles of a computer continue to expand, so to does the threat posed by cyberattacks. An important challenge for today's software engineers is to build secure software and help neutralize these threats. Formal methods have long been suggested as an excellent way to build secure software but have not been widely adopted for this purpose. The "conventional wisdom" has suggested several reasons for this slow adoption, including a steep learning curve, difficulty in augmenting existing systems, and lack of tools with security-specific abstractions. Our hypothesis, however, is that applying a small and easy to learn subset of the techniques available today could significantly decrease software vulnerabilities and reduce the risk of cyberattacks. In this paper, we discuss the motivation for our hypothesis and discuss our ongoing experiment to test it.

Software Defect Prediction Using Machine Learning

Software defect prediction analysis is an important problem in the software engineering community. Software defect prediction can directly affect the quality and has achieved significant popularity in the last few years. This software prediction analysis helps in delivering the best development and makes the maintenance of software more reliable. This is because predicting the software faults in the earlier phase improves the software quality,efficiency, reliability and the overall cost in SDLC. Developing and improving the software defect prediction model is a challenging task and many techniques are introducing for better performance. Supervised ML algorithms have been used to predict future software faults based on historical data[1]. These classifiers are Naïve Bayes (NB), Support Vector Machine(SVM) and Artificial neural network(ANN). The evaluation process showed that ML algorithms can be used effectively with a high accuracy rate. The comparison is made with other machine learning algorithms to finds the algorithms which gives more accuracy. And the results show that machine learning algorithms gives the best performance. The existence of software defects affects dramatically on software reliability, quality, and maintenance cost. Achieving reliable software also is hard work, even the software applied carefully because most time there is hidden errors. In addition, developing a software defect prediction model which could predict the faulty modules in the early phase is a real challenge in software engineering. Software defect prediction analysis is an essential activity in software development. This is because predicting the bugs prior to software deployment achieves user satisfaction, and helps in increasing the overall performance of the software. Moreover, predicting software defects early improves software adaptation to different environments and increases resource utilization.

Auction‐based serious game for bug tracking

Today, one of the challenges in software engineering is utilising application lifecycle management (ALM) tools effectively in software development. In particular, it is hard for software developers to engage with the work items that are appointed to themselves in these ALM tools. In this study, the authors have focused on bug tracking in ALM where one of the most important metrics is mean time to resolution that is the average time to fix a reported bug. To improve this metric, they developed a serious game application based on an auction-based reward mechanism. The ultimate aim of this approach is to create an incentive structure for software practitioners to find and resolved bugs that are auctioned where participants are encouraged to solve and test more bugs in less time and improve quality of software development in a competitive environment. They conduct hypothesis tests by performing a Monte Carlo simulation. The preliminary results of this research support the idea that using a gamification approach for an issue tracking system enhances the productivity and decreases mean time to resolution.

Engineering complex data integration, harmonization and visualization systems

Abstract Complex data transformation, aggregation and visualization problems are becoming increasingly common. These are needed in order to support improved business intelligence and end-user access to data. However, most such applications present very challenging software engineering problems including noisy data, diverse data formats and APIs, challenging data modeling and increasing demand for sophisticated visualization support. This paper describes a data integration, harmonization and visualization process and framework that we have been developing. We discuss our approach used to tackle complex data aggregation and harmonization problems and we demonstrate a set of information visualizations that can be developed from the harmonized data to make it usable for its target audience. We use a case study of Household Travel Survey data mapping, harmonization, aggregation and visualization to illustrate our approach. We summarize a set of lessons that we have learned from this industry-based software engineering experience. We hope these will be useful for others embarking on challenging data harmonization and integration problems. We also identify several key directions and needs for future research and practical support in this area.

Software Engineering for Smart Healthcare Applications

Software engineering has been used by software vendors and consultants in the development of quality health care applications ranging from electronic medical systems, patient record management applications to medical middleware devices. As a discipline, it has evolved over the last decade in the production of high-quality software across many industries. Healthcare applications demand unique expertise tailored to best project methodologies and software development models. Many health care providers argue that best software practices and usercentered design principles are vital to producing quality applications across all domains. Lack of focus on systematic software development process increases flaws in the implementation causing a loss regarding quality, cost, and trust. The survey paper seeks to analyze existing models in the area of Software Engineering and to propose best SDLC model for Smart Healthcare applications which focuses on quality improvement. This survey also includes identifying the research challenges of software engineering for smart applications

Optimization of the Size of Thread Pool in Runtime Systems to Enterprise Application Integration: a Mathematical Modelling Approach

Companies seek technological alternatives that provide competitiveness for their business processes. One of them is integration platforms, software tools that build integration solutions, which allow the different applications that make up the software ecosystem to work synchronously and that new applications or functionalities be incorporated with the least impact in the existing ones. The runtime system is the component of the integration platform responsible for managing the computational resources that run the integration solution. Among these The performance of the runtime systems is directly related to the number of threads available to run the integration solution, but scaling the number of threads that provide a shorter response time is a challenge for software engineers. If this quantity is undersized, it may cause a delay in the execution; if it is overestimated, it could cause a waste of computational resources. This article presents a mathematical model, defined by differential equations, that establishes the optimum number of threads, which maximizes the expected performance gain by minimizing the execution time of the integration solution. In addition, it presents the mathematical model application, which assists the analysis of the expected gain in different architecture scenarios and quantity of threads.

BEAST 2.5: An advanced software platform for Bayesian evolutionary analysis.

Elaboration of Bayesian phylogenetic inference methods has continued at pace in recent years with major new advances in nearly all aspects of the joint modelling of evolutionary data. It is increasingly appreciated that some evolutionary questions can only be adequately answered by combining evidence from multiple independent sources of data, including genome sequences, sampling dates, phenotypic data, radiocarbon dates, fossil occurrences, and biogeographic range information among others. Including all relevant data into a single joint model is very challenging both conceptually and computationally. Advanced computational software packages that allow robust development of compatible (sub-)models which can be composed into a full model hierarchy have played a key role in these developments. Developing such software frameworks is increasingly a major scientific activity in its own right, and comes with specific challenges, from practical software design, development and engineering challenges to statistical and conceptual modelling challenges. BEAST 2 is one such computational software platform, and was first announced over 4 years ago. Here we describe a series of major new developments in the BEAST 2 core platform and model hierarchy that have occurred since the first release of the software, culminating in the recent 2.5 release.

A Progression Model of Software Engineering Goals, Challenges, and Practices in Start-Ups

Context: Software start-ups are emerging as suppliers of innovation and software-intensive products. However, traditional software engineering practices are not evaluated in the context, nor adopted to goals and challenges of start-ups. As a result, there is insufficient support for software engineering in the start-up context. Objective: We aim to collect data related to engineering goals, challenges, and practices in start-up companies to ascertain trends and patterns characterizing engineering work in start-ups. Such data allows researchers to understand better how goals and challenges are related to practices. This understanding can then inform future studies aimed at designing solutions addressing those goals and challenges. Besides, these trends and patterns can be useful for practitioners to make more informed decisions in their engineering practice. Method: We use a case survey method to gather first-hand, in-depth experiences from a large sample of software start-ups. We use open coding and cross-case analysis to describe and identify patterns, and corroborate the findings with statistical analysis. Results: We analyze 84 start-up cases and identify 16 goals, 9 challenges, and 16 engineering practices that are common among start-ups. We have mapped these goals, challenges, and practices to start-up life-cycle stages (inception, stabilization, growth, and maturity). Thus, creating the progression model guiding software engineering efforts in start-ups. Conclusions: We conclude that start-ups to a large extent face the same challenges and use the same practices as established companies. However, the primary software engineering challenge in start-ups is to evolve multiple process areas at once, with a little margin for serious errors.

Autotuning in High-Performance Computing Applications

Autotuning refers to the automatic generation of a search space of possible implementations of a computation that are evaluated through models and/or empirical measurement to identify the most desirable implementation. Autotuning has the potential to dramatically improve the performance portability of petascale and exascale applications. To date, autotuning has been used primarily in high-performance applications through tunable libraries or previously tuned application code that is integrated directly into the application. This paper draws on the authors’ extensive experience applying autotuning to high-performance applications, describing both successes and future challenges. If autotuning is to be widely used in the HPC community, researchers must address the software engineering challenges, manage configuration overheads, and continue to demonstrate significant performance gains and portability across architectures. In particular, tools that configure the application must be integrated into the application build process so that tuning can be reapplied as the application and target architectures evolve.

On the benefits and challenges of using kanban in software engineering: a structured synthesis study

ContextKanban is increasingly being used in diverse software organizations. There is extensive research regarding its benefits and challenges in Software Engineering, reported in both primary and secondary studies. However, these results have not been synthesized yet.Goalto investigate and identify the actual benefits and challenges of using Kanban in Software Engineering to support practitioners in understanding and analyzing the benefits and challenges of adopting Kanban in their software projects.Methodto use the Structured Synthesis Method to aggregate existing empirically-grounded evidence in the published primary studies regarding using Kanban in Software Engineering.Resultsfrom the 20 selected primary studies in which over 16 benefits were identified, four had the most robust results in the aggregation, i.e., with the most confidence associated, namely: ‘work visibility,’ ‘control of project activities and tasks,’ ‘flow of work,’ and ‘time-to-market.’ Furthermore, the ‘organizational culture’ was identified as the most dominant challenge in Kanban implementations.ConclusionsSyntheses studies represent a fundamental step in organizing the body of evidence as an empirically-grounded reference for decision-making in practice. The benefits with most confidence indeed appear to be the ones intrinsically linked to the Lean thinking and the Kanban approach. As Kanban originated in the manufacturing, it is interesting to observe this kind of confirmation in the software domain. Still, there are several benefits and challenges which still lacks the appropriate level of evidence. We also noticed the absence of negative results reported in the technical literature. These aspects need the additional attention of the research community.