Software Practitioners Research Articles

Agile software development one year into the COVID-19 pandemic

As a result of the COVID-19 pandemic, many agile practitioners had to transition into a remote work environment. Despite remote work not being a new concept for agile software practitioners, the forced or recommended nature of remote work is new. This study investigates how the involuntary shift to remote work and how social restrictions imposed by the COVID-19 pandemic have affected agile software development (ASD), and how agile practitioners have been affected in terms of ways of working. An explanatory sequential mixed methods study was performed. Data were collected one year into the COVID-19 pandemic through a questionnaire with 96 respondents and in-depth semi-structured interviews with seven practitioners from seven different companies. Data were analyzed through Bayesian analysis and thematic analysis. The results show, in general, that the aspects of ASD that have been the most affected is communication and social interactions, while technical work aspects have not experienced the same changes. Moreover, feeling forced to work remotely has a significant impact on different aspects of ASD, e.g., productivity and communication, and industry practitioners’ employment of agile development and ways of working have primarily been affected by the lack of social interaction and the shift to digital communication. The results also suggest that there may be a group maturing debt when teams do go back into office, as digital communication and the lack of psychological safety stand in the way for practitioners’ ability to have sensitive discussions and progress as a team in a remote setting.

SPE$^{2}$: Self-Paced Ensemble of Ensembles for Software Defect Prediction

Software defect prediction aims to predict defect-prone code regions automatically before defects are discovered. Accurate prediction helps software practitioners to prioritize their testing efforts. In recent decades, dozens of approaches have been put forward and acquired good results in this field. However, in practical scenarios, many projects have limited labeled instances; more than that, most of these labeled instances are nondefective. The lack of training data and class imbalance problem together bring serious challenges to software defect prediction tasks. So far, few of prevailing approaches can well handle these two difficulties simultaneously. One important reason is that they do not pay adequate attention to several key instances, which are difficult to classify in a small imbalanced dataset. This article introduces the concept of &#x201C;<i>instance hardness</i>&#x201D; to integrate various difficulties of imbalance classification tasks. Based on it, a novel imbalance learning framework named self-paced ensemble of ensembles (SPE<inline-formula><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula>) is proposed to perform software defect prediction. SPE<inline-formula><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> aims to generate a strong ensemble of ensembles by self-paced harmonizing instance hardness via undersampling. Finally, SPE<inline-formula><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> is extensively compared with eight imbalance learning approaches on ten open-source defect datasets. Experiments indicate that SPE<inline-formula><tex-math notation="LaTeX">$^{2}$</tex-math></inline-formula> improves the performance and achieves better and more significant F-measure values than its existing counterparts, based on Brunner&#x2019;s statistical significance test and Cliff&#x2019;s effect sizes.

Generative Oversampling Methods for Handling Imbalanced Data in Software Fault Prediction

Imbalanced software fault datasets, having fewer faulty modules than the nonfaulty modules, make accurate fault prediction difficult. It is challenging for software practitioners to handle imbalanced fault data during software fault prediction (SFP). Earlier, several researchers have applied oversampling techniques such as synthetic minority oversampling techniques and others for imbalanced learning in SFP. However, most of these techniques resulted in overfitted prediction models. This article presents generative oversampling methods to handle imbalanced data problems in the SFP. Using the generative adversarial network (GAN) based approach, the presented methods generate synthetic samples of the faulty modules to balance the proportion of faulty and nonfaulty modules in the fault datasets. Further, SFP models are built on the processed fault datasets using different machine learning techniques. Experimental validation of the presented oversampling methods is done on 18 fault datasets gathered from PROMISE, JIRA, Eclipse data repositories, and precision, recall, f1-score, and AUC are used as evaluation measures. We extensively compared presented oversampling methods with various state-of-the-art class imbalance techniques and baseline models. The experimental results evidenced that the presented methods improved fault prediction performance and yielded better performance than the state-of-the-art class imbalance techniques.

Feature toggles as code: Heuristics and metrics for structuring feature toggles

Using feature toggles is a technique to turn a feature either on or off in program code by checking the value of a variable in a conditional statement . This technique is increasingly used by software practitioners to support continuous integration and continuous delivery (CI/CD). However, using feature toggles may increase code complexity, create dead code, and decrease the quality of a codebase. The goal of this research is to aid software practitioners in structuring feature toggles in the codebase by proposing and evaluating a set of heuristics and corresponding complexity, comprehensibility , and maintainability metrics based upon an empirical study of open source repositories. We identified 80 GitHub repositories that use feature toggles in their development cycle. We conducted a qualitative analysis using 60 of the 80 repositories to identify heuristics and metrics. Then, we conducted a survey of practitioners of 80 repositories to obtain their feedback that the proposed heuristics can be used to guide the structure of feature toggles and to reduce technical debt. We also conducted a case study of the all 80 repositories to analyze relations between heuristics and metrics. From the qualitative analysis, we proposed 7 heuristics to guide structuring feature toggles and identified 12 metrics to support the principles embodied in the heuristics. Our survey result shows that practitioners agree that managing feature toggles is difficult, and using identified heuristics can reduce technical debt. Based on our case study, we find a relationship between the adoption of heuristics and the values of metrics. Our results support that practitioners should have self-descriptive feature toggles, use feature toggles sparingly, avoid duplicate code in using feature toggles, and ensure complete removal of a feature toggle.

Factors affecting Agile adoption: An industry research study of the mobile app sector in Saudi Arabia

Agile is an established software development methodology that helps develop software by improving time to market, quality, customer engagement and reducing costs. Factors underpinning its adoption have been widely researched. However, most of these studies have been conducted in developed countries, particularly Europe and North America, with only a handful carried out in developing countries, including the Middle East. This is problematic given the strategic and economic importance of the software industry in such places as Saudi Arabia, where Agile adoption remains in the early stages, despite heavy investment in this industry in recent years to diversify its oil-dependent economy. Therefore, this study empirically investigates the factors influencing Agile adoption by Saudi Arabian software small and medium-sized enterprises (SMEs). To this end, in-depth interviews were conducted with 12 software practitioners from three software SMEs. Our findings reveal that human factors, such as customer involvement, team capability, organisational culture and national culture, are considered the most impactful factors affecting the adoption of Agile as opposed to technical ones, such as the availability of specific tools or techniques.

A Text Mining Framework for Analyzing Change Impact and Maintenance Effort of Software Bug Reports

Software practitioners often strive to achieve a “bug-free” software, though, it is a myth. Software Bug Categorization (SBC) models, which assigns levels (viz. “low”, “moderate” or “high”) to a software bug aid effective bug management. They assist in allocation of proper maintenance resources for bug elimination to improve software quality. This study proposes the development of SBC models that allocate levels on the basis of three software bug aspects i.e., maintenance effort required to correct a bug, its change impact and the combined effect of both of these. In order to develop SBC models, we use text mining approach, which extracts relevant features from bug descriptions and relates these features with different software bug levels. The results of the study indicate that the categorization of software bugs in accordance with maintenance effort and change impact is possible. Furthermore, the combined approach SBC models were also found to be effective.

Simplified abstract syntax tree based semantic features learning for software change prediction

AbstractSoftware change prediction aims to identify the change‐prone parts of source code, which can help software practitioners allocate resources more efficiently, increase the quality of software products, and reduce maintenance costs. In recent years, researchers have built many change prediction models based on product and process metrics using traditional classification algorithms. However, source code contains rich semantic structural information, which traditional features cannot usually capture. Therefore, extracting the semantic features of code can help improve the performance of existing models. To bridge the gap between semantic features and change prediction, we introduce a novel change prediction approach based on a simplified abstract syntax tree (AST). Specifically, we first extract semantic features from partial AST nodes that pay attention to the syntax and semantic of code instead of all AST nodes. Then, a bidirectional recurrent neural network is utilized to model the deep semantic information of the code for change prediction. We also propose a new dataset that to some extent alleviates the data‐imbalance problem, which has become an active research topic. We conducted extensive experiments on the proposed dataset. The results show the effectiveness of semantic features for change prediction. Further, our model outperformed a state‐of‐the‐art code representation method.

Formalization of the prediction and ranking of software development life cycle models

The study of software engineering professional practices includes the use of the formal methodology in a software development. Identifying the appropriate methodology will not only reduce the failure of software but will also help to deliver the software in accordance with the predetermined budget and schedule. In literature, few works have been developed a tool for prediction of the most appropriate methodology for the specific software project. In this paper, a method for selecting an appropriate software development life cycle (SDLC) model based on a ranking manner from the highest to the lowest scoring is presented. The selection and ranking of appropriate SDLC elaborate the related SDLC’s critical factors, these factors are given different weights according to the SDLC, then these weights are used by the proposed mathematical method. The proposed approach has been extensively experimented on a dataset by software practitioners who are working in the software industry. Experimental results show that, the proposed method represents an applicable tool in predicting and ranking suitable SDLC models on various types of projects, such as: life-critical systems, commercial uses systems, and entertainment applications.

SECRS TEMPLATE TO AID NOVICE DEVELOPERS IN SECURITY REQUIREMENTS IDENTIFICATION AND DOCUMENTATION

The security requirements are one of the non-functional requirements (NFR) which acts as a constraint on the functions of the system to be built. Security requirements are important and may affect the entire quality of the system. Unfortunately, many organizations do not pay much attention to it. The security problems should be focused on the early phases of the development process i.e. in the requirements phase to stop the problems spreading down in the later phases and in turn to avoid the rework. Subsequently, when security requirements are to be focused, proper guidance should be provided which should assist requirements engineers. Many security requirements engineering methods were developed in the past which require different level of expertise such as SQUARE process which requires requirements engineer to have a certain level of security expertise. Moreover, it lacks proper guidance especially for novice developers in applying the existing security requirements engineering (SecRE) methods to identify security requirements. Hence, this study intends to address the gap by developing a guided template to assist novice developers in the security requirements identification and documentation. The main objectives of the research are: 1) to study and investigate the existing security requirements engineering (SecRE) methods. 2) To develop a template to aid novice developers in identifying and documenting security requirements. The developed template is applied to two case studies of software projects to determine its usability and applicability. The results of the case studies evaluation show that both the usability and applicability of the template is good. The template is also evaluated by several experts and software practitioners. The evaluation results show that the SecRS template is found to be satisfying the usability and applicability factors; thereby confirming that the proposed template achieves its desired objective of aiding the novice developers to identify and document security requirements correctly.

Supporting refactoring of BDD specifications—An empirical study

Behavior-driven development (BDD) is a variant of test-driven development where specifications are described in a structured domain-specific natural language. Although refactoring is a crucial activity of BDD, little research is available on the topic. To support practitioners in refactoring BDD specifications by (1) proposing semi-automated approaches to identify refactoring candidates; (2) defining refactoring techniques for BDD specifications; and (3) evaluating the proposed identification approaches in an industry context. Using Action Research, we have developed an approach for identifying refactoring candidates in BDD specifications based on two measures of similarity and applied the approach in two projects of a large software organization. The accuracy of the measures for identifying refactoring candidates was then evaluated against an approach based on machine learning and a manual approach based on practitioner perception. We proposed two measures of similarity to support the identification of refactoring candidates in a BDD specification base; (1) normalized compression similarity (NCS) and (2) similarity ratio (SR). A semi-automated approach based on NCS and SR was developed and applied to two industrial cases to identify refactoring candidates. Our results show that our approach can identify candidates for refactoring 6o times faster than a manual approach. Our results furthermore showed that our measures accurately identified refactoring candidates compared with a manual identification by software practitioners and outperformed an ML-based text classification approach. We also described four types of refactoring techniques applicable to BDD specifications; merging candidates, restructuring candidates, deleting duplicates, and renaming specification titles. Our results show that NCS and SR can help practitioners in accurately identifying BDD specifications that are suitable candidates for refactoring, which also decreases the time for identifying refactoring candidates.

Exploring How to Apply Secure Software Design Principles

Secure design principles (SDPs) are employed to be a solution against many types of attacks. However, it has been shown that software developers are not familiar with the notion of SDPs or do not know how to implement them in the design stage. This paper tries to bridge this gap by applying SDPs to a real-world software project, electronic promotion system (ePS), and commenting on the contribution of each SDP. Saltzer and Schroeder’s eight principles, along with three additional principles proposed by others, are chosen to be applied to ePS. The results indicate that most of the principles enumerated here were instrumental and applied in the ePS’s design. Most of the eleven SDPs, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">economy of mechanism, fail-safe defaults, least privilege, least common mechanisms, sound authentication, defense in depth</i> , and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">input validation</i> were implemented on ePS to a great extent. Others, namely <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">separation of privileges</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">psychological acceptability</i> , were applied to a limited extent. The remaining two principles, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">complete mediation</i> and <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">open design</i> , did not play a vital role, as ePS by itself satisfies these two principles. Some contradictions and interrelations among the SDPs when they were applied were also debated. Taking into account the integration of ePS with other enterprise systems in the same organization, it was felt placing SDPs in a general context would be beneficial and sufficient. This work will bridge the gap between software practitioners and state-of-the-art research on software SDPs.

An approach to explore sequential interactions in cognitive activities of software engineering

: The study of cognitive aspects around software activities can provide valuable insights to improve software engineering practices. Objective: This paper presents an approach based on distributed cognition and sequential analysis to explore cognitive activities in the software development context by analyzing the interactions between software practitioners and the resources used to support them. Method: We conducted nine laboratory-based observation sessions to record qualitative audio/video data of interactions between the study participants and at-hand resources during the planning and managing of software analytics tasks. Results: The interaction strategies of the resources model included 21 emergent actions, and the sequential analysis revealed two different patterns of interaction over time. Conclusion: Our approach has been useful for evaluating how well an artifact works to support a team in software analytics activities. Furthermore, it can be applied to explore and discover interaction patterns in different software activities.

Sustainable-Security Assessment Through a Multi Perspective Benchmarking Framework

The current cyber-attack environment has put even the most protected systems at risk as the hackers are now modifying technologies to exploit even the tiniest of weaknesses and infiltrate networks. In this situation, it's critical to design and construct software that is both secure and long-lasting. While security is the most well-defined aspect of health information software systems, it is equally significant to prioritise sustainability because any health information software system will be more effective if it provides both security and sustainability to the customers at the same time. In this league, it is crucial to determine those characteristics in the systems that can help in the accurate assessment of the sustainable-security of the health information software during the development stage. This research work employed the Fuzzy Analytic Network Process (Fuzzy ANP) to estimate the impact of the overall sustainable-security of health information software systems and their characteristics in order to achieve a high level of sustainable-security. Furthermore, the study validates the efficacy of the Fuzzy ANP procedure by testing it on five different versions of a health information software system through Fuzzy Technique for Order of Preference by Similarity to Ideal Solutions (Fuzzy TOPSIS). Despite the sensitivity of the health information software systems, this study employed multiple versions of health information software system. When compared with the existing procedures for testing the sustainable-security of health information software systems, the outcomes were conclusive and significantly more effective. Besides saving time and resources, the mechanism suggested in this research work aims to establish an outline that software practitioners can follow to enhance the sustainable-security of health information software systems.

BW-TOPSIS: A Hybrid Method to Evaluate Software Testing Techniques

Software testing plays a significant role in various software development phases. There are so many software testing techniques available. Selecting the most suitable software testing technique based on multiple factors is challenging for software practitioners. This paper proposes an MCDM-based hybrid approach for selecting the most appropriate software testing technique among various available software testing techniques, considering multiple factors such as cost, schedule, resources, etc. Because of the involvement of multiple factors, the problem of selecting the most appropriate software testing technique can be modeled as an MCDM problem. This study proposes a hybrid approach by integrating two MCDM methods BWM (Best-Worst Method) and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution), for evaluating various software testing techniques considering multiple factors altogether. For the applicability of the proposed approach, an experimental study was conducted using seven software testing techniques and six evaluation criteria. Results show the proposed approach can be used as an efficient tool for selecting the most suitable software testing technique among various available testing techniques in the presence of multiple factors.

Facilitating Reuse of Functions in Embedded Software

Developing software-intensive embedded systems is a significant challenge as embedded systems have become more complex and dynamic for integrating various constraints (e.g., real-time performance, design, and functional safety). In general, software practitioners reuse code to reduce development time for implementing required functionalities. However, embedded software development still lacks code reuse on a large scale due to its intricate constraints. This paper presents an approach to identifying reusable functions and their relationships from the legacy embedded software for faster development reducing functionality testing time. A python tool is developed to automatically extract the reusable functions and present them in a feature model. The search time for finding reusable functions speeds up when they are placed as features in a feature model. We conducted experiments on three GitHub embedded software projects (elevator, infotainment, and health monitoring systems). The experimental results find that our proposed approach reduces the feature search time by 59.0%, 49.8%, and 74.5%, respectively, compared to the manual searching approach for 50 features.

Quality Requirement Documentation Guidelines for Agile Software Development

In agile software development (ASD), where minimal documentation and rapid delivery are the focus, Quality requirements (QRs) are often underspecified and not documented. Guidelines supporting QR documentation task are scarce. The study developed the Agile QR-Doc QR documentation guidelines, which aim to support QR documentation in ASD. We applied a design science research methodology (DSRM) to build the Agile QR-Doc. We used a survey questionnaire and open discussion with ten software practitioners, from two ASD companies to validate the Agile QR-Doc. The practitioners evaluated the guidelines in terms of usefulness, relevance, understandability, and coverage of important aspects for supporting QR documentation and their impact on the agility of the software development process. Agile QR-Doc list 12 recommendations that are grouped into two categories. The first category introduces three recommendations that focus on raising awareness about the significance of QRs, their documentation and related challenges. The second category lists nine recommendations that introduce artifacts, practices and important aspects for documenting QRs. The validation reveals the relevance, understandability and usefulness of the guidelines to support QR documentation in ASD. It also indicates that the guidelines consider important aspects for documenting QRs and that they do not negatively affect the agility of the software development process. Practitioners can utilize the practices, artifacts and knowledge from the guidelines to support QR documentation in ASD. Researchers can benefit from the knowledge on QR documentation in ASD, and application of DSRM in building artifacts.

A Decentralized Framework for Managing Task Allocation in Distributed Software Engineering

In distributed software development, planning and managing fair and transparent task allocation is both critical and challenging. The objective of this paper is to propose a decentralized blockchain-oriented, transparent task allocation framework to improve the quality of the task allocation process. It addresses the concerns of (i) enhancing collaboration, (ii) inhibiting knowledge vaporization, and (iii) reducing documentation problems. The proposed method is a novel two-fold process: First, it identifies and categorizes tasks exhibiting different dependencies and complexities to create equal task clusters based on their dependency type, difficulty, cost, and time. Second, it uses a blockchain-oriented framework to broadcast, check bid validity, allow developers to bid on tasks matching their roles and expertise, evaluate, and announce the winner for task allocation using smart contracts. Results of experimentation, surveys, and interviews with software practitioners conclude that the proposed solution is transparent and effective in allocating tasks (with Cranach’s alpha of 0.894) at a low cost of contract execution in a distributed software development environment. Overall, the proposed approach will have a positive and significant impact in industrial settings.

Home-Based Working During Covid-19 Pandemic: Experience of Turkish Software Professionals

This study explores the home-based working experience of software professionals during Covid-19 pandemic. Through an online survey, it examines the challenges of home based working in relation to characteristics of such work settings. Furthermore, it investigates the relation between home-based working characteristics and self-reported productivity via bivariate analysis. The results of this study show that it was mostly easy to adopt to the home-based new work setting while most of the software practitioners stated that they worked longer hours during the pandemic period. Housework and kids were reported as the two primary interruptions in home based setting. Additionally, afternoons and mornings were mentioned as the most productive work intervals for software professionals.

Modeling knowledge and practices in the software industry: An exploratory study of Turkey-educated practitioners

Modeling helps software professionals to achieve various purposes via different approaches. Modeling stakeholders, who have different academic backgrounds (e.g., not only based on ”computing disciplines”), might be lacking knowledge in key modeling topics, which might be learned or improved at the workplace. To better align the modeling skills learnt at the academia with the expectations of the industry, it is important to analyze the relation between the educational skill-set and the modeling practices of the practitioners based on various modeling approaches. Moreover, understanding the-state-of-the-practices for modeling characteristics (e.g., purposes, languages, software development lifecycle phases, where modeling is used) is also necessary to provide a view on various modeling approaches used in the software industry. To achieve these goals, we designed and conducted a practitioner survey. 628 software practitioners, whose undergraduate degree was completed in Turkey, working in 13 countries responded the survey. This paper sheds light on the latest modeling practices with various cross-factor analyses by analyzing the relation of educational background with those modeling practices. Investigating how the way(s) of learning about software modeling affect related practices helps bridge the knowledge gaps. We believe that our findings would provide practical benefits to all modeling professionals (from developers to systems engineers and testers) and educators by influencing both the academia (mostly Turkish universities) and the industry.

Discussion of Agile Software Development Methodology and its Relevance to Software Engineering

Agile Software Development Methodology is a lesser-known and infrequently utilized methodology in academia. In reality, though, content developer software practitioners frequently employ this technique. This journal was created to give readers an overview of agile techniques and their use at various stages of software development in general.