Software Development Tasks Research Articles

Enhancing supervised bug localization with metadata and stack-trace

Locating relevant source files for a given bug report is an important task in software development and maintenance. To make the locating process easier, information retrieval methods have been widely used to compute the content similarities between bug reports and source files. In addition to content similarities, various other sources of information such as the metadata and the stack-trace in the bug report can be used to enhance the localization accuracy. In this paper, we propose a supervised topic modeling approach for automatically locating the relevant source files of a bug report. In our approach, we take into account the following five key observations. First, supervised modeling can effectively make use of the existing fixing histories. Second, certain words in bug reports tend to appear multiple times in their relevant source files. Third, longer source files tend to have more bugs. Fourth, metainformation brings additional guidance on the search space. Fifth, buggy source files could be already contained in the stack-trace. By integrating the above five observations, we experimentally show that the proposed method can achieve up to 67.1% improvement in terms of prediction accuracy over its best competitors and scales linearly with the size of the data.

On Moderating Software Crowdsourcing Challenges

Crowdsourcing divides a task into small pieces that are carried out by the crowd. In Software Engineering, crowdsourcing divides the software development tasks of to be carried out online by the crow and is simply called Software Crowdsourcing (SW CS). Most SW CS platforms support this emerging software development strategy and operate within a framework of competition among the crowd. Competitive SW CS platforms intentionally minimize communication and collaboration among the parties involved (customer, platform, and crowd) while they compete in the software development tasks. The goal of this paper is to investigate platform moderators in SW CS challenges. Platform moderators are individuals who work for the SW CS platforms to mediate customer and crowd. A qualitative analysis of the content of the communication forums hosted on the TopCoder platform was performed to analyze the messages exchanged by the platform moderators and the crowd. Our empirical results indicate that co-pilots enforce and, at the same time, extend the limitations of the documentation associated with the tasks to support crowd members, provide technical help to crowd members during the competitions, and engage the crowd in the challenges. Co-pilots are organized, work diligently, worrying about being fair, and, at the same time, seeking to find a balance between autonomy and dependency on the customer. We conclude by providing insights to improve the design of software crowdsourcing platforms.

Crowdsourcing in Software Development: Empirical Support for Configuring Contests

Despite the extensive adoption of crowdsourcing for the timely, cost-effective, and high-quality completion of software development tasks, a large number of crowdsourced challenges are not able to acquire a winning solution, on time, and within the desired cost and quality thresholds. A possible reason for this is that we currently lack a systematic approach that would aid software managers during the process of designing software development tasks that will be crowdsourced. This paper attempts to extend the current knowledge on designing crowdsourced software development tasks, by empirically answering the following management questions: (a) what type of projects should be crowdsourced; (b) why should one crowdsource-in terms of acquired benefits; (c) where should one crowdsource-in terms of application domain; (d) when to crowdsource-referring to the time period of the year; (e) who will win or participate in the contest; and (f) how to crowdsource (define contest duration, prize, type of contest etc.) to acquire the maximum benefits-depending on the goal of crowdsourcing. To answer the aforementioned questions, we have performed a case study on 2,209 software development tasks crowdsourced through TopCoder platform. The results suggest that there are significant differences in the level to which crowdsourcing goals are reached, across different software development activities. Based on this observation we suggest that software managers should prioritize the goals of crowdsourcing, decide carefully upon the activity to be crowdsourced and then define the settings of the task.

A hybrid collaborative filtering recommendation algorithm for requirements elicitation

Requirements elicitation is one of the most critical and difficult tasks in software development. Requirements reuse has shown to be an effective and efficient elicitation technique that can enhance the quality of the requirements elicitation process and, as a result, lead to a project's success. However, the information overload problem, which is caused by the rapidly growing number of reusable software requirements in large requirements repositories, hinders the effectiveness of the requirements reuse process. Recommender systems proved to be a well-known solution to such problems. This paper focuses on the adoption of recommender systems to mitigate the problem of information overload that is inherent in the requirements elicitation process, specifically by assisting requirement engineers in retrieving relevant reusable requirements from large-scale requirements repositories. The validation results on the RALIC dataset illustrate that the proposed algorithm outperforms and mitigates the drawbacks of the benchmark collaborative filtering-based recommendation approaches.

A hybrid collaborative filtering recommendation algorithm for requirements elicitation

Requirements elicitation is one of the most critical and difficult tasks in software development. Requirements reuse has shown to be an effective and efficient elicitation technique that can enhance the quality of the requirements elicitation process and, as a result, lead to a project's success. However, the information overload problem, which is caused by the rapidly growing number of reusable software requirements in large requirements repositories, hinders the effectiveness of the requirements reuse process. Recommender systems proved to be a well-known solution to such problems. This paper focuses on the adoption of recommender systems to mitigate the problem of information overload that is inherent in the requirements elicitation process, specifically by assisting requirement engineers in retrieving relevant reusable requirements from large-scale requirements repositories. The validation results on the RALIC dataset illustrate that the proposed algorithm outperforms and mitigates the drawbacks of the benchmark collaborative filtering-based recommendation approaches.

A comprehensive review on software comprehension models

Software comprehension is one of the most important among software development tasks since most developers do not start a brand new software every time they switch jobs or get transferred from one project to another but join long-running software projects. Every experienced and expert developer has their own established methods of understanding complex software systems. These methods might be different for everyone but they still have common aspects by which multiple well-defined code comprehension models can be constructed. Furthermore, the degree of understanding of a software can be categorized as well, according to the ability of the programmer to modify or develop a certain part of the software system. This paper is intended to provide a review of the cognitive software comprehension models established by extensive research in this topic as well as describe the dimensions of understanding software. It also determines the editor support of cognition models by examining common editor functionalities and categorizing code editors based on the availability of functionalities of each cognition approach.

Particle Swarm Optimisation Based Feature Selection for Software Effort Prediction Using Supervised Machine Learning and Ensemble Methods: A Comparative Study

An essential goal of software cost prediction is predicting accurate costs, which is a vital part of the software process. Individuals with various skills carry out most software development tasks, so efforts are measured by person-months and time. The overestimation and underestimation of developing costs is a future disappointment. Additionally, software venture managers will determine the cost of software development. The prevailing expense for any software is the cost of ascertaining efforts, making software cost estimation a crucial task. The past three decades have seen various approaches to software cost estimations based on software metrics. However, it is difficult to decide which model offers a more precise estimate on the data set. This article uses three different data sets: Albrecht, Desharnais and COCOMONASA. Classification often includes many features in a data set, but not all are useful for classification. Abusive and repetitive symptoms reduce performance, so this article uses particle swarm optimisation as a feature selection algorithm for different machine learning methods like Random Forest, REPTree, SMOReg, Linear Regression, M5Rule and MLP. At the same time, bagging is also used with said base learners. The bagging M5Rule gives the best performance with an MMRE value of 28.78%.

Parameter Estimation Techniques of Software Reliability Growth Models: A Critical Research with Experimentation

Ensuring software reliability is a challenging task in software development. Software reliability is the probability of software to provide its intended functionality over a specified time. A couple of testing procedures during the phases of software development provides the data to approximate software reliability. This approximation guides the development team to plan necessary corrective actions. A variety of Software Reliability Growth Models (SRGMs) are in use to predict software reliability. A common task for every SRGM is to calculate reliability growth models attributes as a part of reliability estimation. Optimal calculation of such attributes is influenced by the relationships among the parameters of an SRGM. Therefore parametric SRGMs rely on parameter estimation techniques. The present paper has undertaken the study of existing parameter estimation techniques with the main goal of understanding the pros and cons of each technique in order to design a better technique of parameter estimation for SRGM’s in use. A critical review of existing techniques of parameter techniques is given in this paper detailing the categories, approaches, problems relating to the techniques. One of the most successful swam intelligence techniques named Gray Wolf Optimization (GWO) along with its variants are applied to estimate the parameters of SRGMs. The results of this application along with the comparative analysis are given. The variants of GWO played a significant role in parameter estimation of the SRGMs considered for the experiments. An attempt is made to propose new ways of parameter estimation to achieve optimization.

Software Reliability Growth Models: A Critical Research with Experimentation for Parameter Estimation

Ensuring software reliability is a challenging task in software development. Software reliability is the probability of software to provide its intended functionality over a specified time. A couple of testing procedures during the phases of software development provides the data to approximate software reliability. This approximation guides the development team to plan necessary corrective actions. A variety of Software Reliability Growth Models (SRGMs) are in use to predict software reliability. A common task for every SRGM is to calculate reliability growth models attributes as a part of reliability estimation. Optimal calculation of such attributes is influenced by the relationships among the parameters of an SRGM. Therefore parametric SRGMs rely on parameter estimation techniques. The present paper has undertaken the study of existing parameter estimation techniques with the main goal of understanding the pros and cons of each technique in order to design a better technique of parameter estimation for SRGM’s in use. A critical review of existing techniques of parameter techniques is given in this paper detailing the categories, approaches, problems relating to the techniques. One of the most successful swam intelligence techniques named Gray Wolf Optimization (GWO) along with its variants are applied to estimate the parameters of SRGMs. The results of this application along with the comparative analysis are given. The variants of GWO played a significant role in parameter estimation of the SRGMs considered for the experiments. An attempt is made to propose new ways of parameter estimation to achieve optimization.

Machine Learning Algorithm Application in Software Quality Improvement using Metrics

Machine learning purely concerned on the concept with building the program that improves the tasks performance through experience. Machine learning algorithms have proven to be of great practical value in a variety of application domains. the field of software engineering turns out to be a fertile ground where many software development tasks could be formulated as learning problems, analyzing design and testing plays the major role and approached in terms of learning algorithms We discuss several metrics in each of five types of software quality metrics: product quality, in-process quality, testing quality, maintenance equality, and customer satisfaction quality.

Big-Five Personality Traits Mapped with Software Development Tasks to Find Most Productive Software Development Teams

The software development lifecycle defines a systematic way of developing good quality software to meet customer expectations about scope, time and budget. Each phase of this development lifecycle is distinct and rigorous which defines an independent body-of-knowledge within the domain of software engineering. Consequently, each phase of development lifecycle demands a different set of skills and personality traits for software engineers to cope-up their work effectiveness to improve the software development process. Hence, it is obligatory to highlight the most relevant personality traits of software engineers working for different phases of development lifecycle. In this regard, this article presents a comprehensive study in which the most relevant personality traits of software engineers have been highlighted for different tasks/phases of software development lifecycle. The big-five personality model has been used to evaluate and correlate the personality of software engineers for each phase due to its wide acceptance in the research and development community.

Sequence effects in the estimation of software development effort

Currently, little is known about how much the sequence in which software development tasks or projects are estimated affects judgment-based effort estimates. To gain more knowledge, we examined estimation sequence effects in two experiments. In the first experiment, 362 software professionals estimated the effort of three large tasks of similar sizes, whereas in the second experiment 104 software professionals estimated the effort of four large and five small tasks. The sequence of the tasks was randomised in both experiments. The first experiment, with tasks of similar size, showed a mean increase of 10% from the first to the second and a 3% increase from the second to the third estimate. The second experiment showed that estimating a larger task after a smaller one led to a mean decrease in the estimate of 24%, and that estimating a smaller task after a larger one led to a mean increase of 25%. There was no statistically significant reduction in the sequence effect with higher competence. We conclude that more awareness about how the estimation sequence affects the estimates may reduce potentially harmful estimation biases. In particular, it may reduce the likelihood of a bias towards too low effort estimates.

Web service API recommendation for automated mashup creation using multi-objective evolutionary search

Modern software development builds on external Web services reuse as a promising way that allows developers delivering feature-rich software by composing existing Web service Application Programming Interfaces, known as APIs. With the overwhelming number of Web services that are available on the Internet, finding the appropriate Web services for automatic service composition, i.e., mashup creation, has become a time-consuming, difficult, and error-prone task for software designers and developers when done manually. To help developers, a number of approaches and techniques have been proposed to automatically recommend Web services. However, they mostly focus on recommending individual services. Nevertheless, in practice, service APIs are intended to be used together forming a social network between different APIs, thus should be recommended collectively. In this paper, we introduce a novel automated approach, called SerFinder, to recommend service sets for automatic mashup creation. We formulate the service set recommendation as a multi-objective combinatorial problem and use the non-dominated sorting genetic algorithm (NSGA-II) as a search method to extract an optimal set of services to create a given mashup. We aim at guiding the search process towards generating the adequate compromise among three objectives to be optimized (i) maximize services historical co-usage, (ii) maximize services functional matching with the mashup requirements, and (iii) maximize services functional diversity. We perform a large-scale empirical experiment to evaluate SerFinder on a benchmark of real-world mashups and services. The obtained results demonstrate the effectiveness of SerFinder in comparison with recent existing approaches for mashup creation and services recommendation. The statistical analysis results provide an empirical evidence that SerFinder, significantly outperforms four state-of-the-art widely-used multi-objective search-based algorithms as well as random search.

A single fault localization technique based on failed test input

Abstract Testing and debugging are very important tasks in software development. Fault localization is a very critical activity in the debugging process and also is one of the most difficult and time-consuming activities. The demand for effective fault localization techniques that can aid developers to the location of faults is high. In this paper, a fault localization technique based on complex network theory named FLCN-S is proposed to improve localization effectiveness on single-fault subject programs. The proposed technique diagnoses and ranks faulty program statements based on their behavioral anomalies and distance between statements in failed tests execution by utilizing two network centrality measures (degree centrality and closeness centrality). The proposed technique is evaluated on a well-known standard benchmark (Siemens test suite) and four Unix real-life utility subject programs (gzip, sed, flex, and grep). Overall, the results show that FLCN-S is significantly more effective in locating faults in comparison with other techniques. Furthermore, we observed that both degree and closeness centrality play a vital role in the identification of faults.

On the challenges novice programmers experience in developing IoT systems: A Survey

The co-existence of various kinds of devices, protocols, architectures, and applications make Internet of Things (IoT) systems complex to develop, even for experienced programmers. When novice programmers are learning to implement these systems, they are required to deal with areas in which they do not have a deep knowledge. Furthermore, besides becoming proficient in these areas separately, they should integrate them and build a system whose components are heterogeneous from both software and hardware perspectives.The accurate understanding of the most challenging issues that novices face is fundamental to envision strategies aimed at easing the development of IoT systems. This paper focuses on identifying such issues in terms of software development tasks that novice programmers encounter when working on IoT systems. To this end, a survey was conducted among 40 novice developers that worked in groups developing IoT systems during several years of a university course. Based on their own experiences, individually and as a group, the most challenging development tasks were identified and prioritized over a common architecture, in terms of difficulty level and efforts. In addition, qualitative data about the causes of these issues was collected and analyzed. Finally, the paper offers critical insights and points out possible future work.

Comparison of Software Design Models

Model comparison has been widely used to support many tasks in model-driven software development. For this reason, many techniques of comparing them have been proposed in the last few decades. However, academia and industry have overlooked a classification of currently available approaches to the comparison of design models. Hence, a thorough understanding of state-of-the-art techniques remains limited and inconclusive. This article, therefore, focuses on providing a classification and a thematic analysis of studies on the comparison of software design models. We carried out a systematic mapping study following well-established guidelines to answer nine research questions. In total, 56 primary studies (out of 4,132) were selected from 10 widely recognized electronic databases after a careful filtering process. The main results are that a majority of the primary studies (1) provide coarse-grained techniques of the comparison of general-purpose diagrams, (2) adopt graphs as principal data structure and compare software design models considering structural properties only, (3) pinpoint commonalities and differences between software design models rather than assess their similarity, and (4) propose new techniques while neglecting the production of empirical knowledge from experimental studies. Finally, this article highlights some challenges and directions that can be explored in upcoming studies.

Are DevOps and Automation Our Next Silver Bullet?

Fred brooks, in his well-known classic The Mythical Man-Month , already told the software engineering industry in 1975 that there are no silver bullets in gaining an order-of-magnitude improvement in software productivity.1 He also observed that most of the big past gains in software productivity have come from removing artificial barriers that have made the accidental tasks inordinately hard, such as severe hardware constraints, awkward programming languages, lack of machine time. The hope and goal of software development processes in orchestrating the essential and accidental software engineering and development tasks is precisely to remove artificial barriers to delivering better, faster, cheaper software to the users. Our next silver bullet seems to have emerged as automating repeatable, manual process tasks. While, on one hand, we debate how to scale agile, on the other, we run to DevOps, continuous integration, and continuous delivery tools to achieve the so-called orders of magnitude of productivity improvement.

Identify Severity Bug Report with Distribution Imbalance by CR-SMOTE and ELM

Manually inspecting bugs to determine their severity is often an enormous but essential software development task, especially when many participants generate a large number of bug reports in a crowdsourced software testing context. Therefore, boosting the capabilities of methods of predicting bug report severity is critically important for determining the priority of fixing bugs. However, typical classification techniques may be adversely affected when the severity distribution of the bug reports is imbalanced, leading to performance degradation in a crowdsourcing environment. In this study, we propose an enhanced oversampling approach called CR-SMOTE to enhance the classification of bug reports with a realistically imbalanced severity distribution. The main idea is to interpolate new instances into the minority category that are near the center of existing samples in that category. Then, we use an extreme learning machine (ELM) — a feedforward neural network with a single layer of hidden nodes — to predict the bug severity. Several experiments were conducted on three datasets from real bug repositories, and the results statistically indicate that the presented approach is robust against real data imbalance when predicting the severity of bug reports. The average accuracies achieved by the ELM in predicting the severity of Eclipse, Mozilla, and GNOME bug reports were 0.780, 0.871, and 0.861, which are higher than those of classifiers by 4.36%, 6.73%, and 2.71%, respectively.

ANALYSIS OF CHARACTER VALUES THROUGH ITP AND ATP TECHNOLOGY AS ONE OF THE INNOVATIONS IN LEARNING ELEMENTARY SCHOOL EDUCATION TO OVERCOME DEGRADATION

<em>The development of time because of the existence of globalization led to moral degradation in children. To overcome the moral degradation of the child, it is necessary to have learning innovations in improving the quality of basic education which requires an analysis of character education using methods of observation and questionnaire by entering the results of analysis into ITP software (Development Task Inventory) and ATP (Analysis of Development Task). The existence of ITP and ATP can identify the level of development of aspects related to character education in primary school. Based on preliminary observations, it was found that many teachers did not know the level of children's character development. The article aims to: (1) educators can identify the level of students character (2) educators can design good learning based on the results of each student's character analysis. The conclusion of this study is that educators can develop children's character levels.</em>

An Empirical Study on Competitive Crowdsource Software Development: Motivating and Inhibiting Factors

Crowdsourcing is gaining more and more popularity among the academic and industrial community. Organizations are adopting this technological advent and increasingly crowdsourcing their tasks to the unknown individuals. However, in the context of competitive crowdsourcing software development (CCSD), crowdsourcing is still unexplored. Too little is presently known about what intricate developers to participate in crowdsourcing software development competitions. Most importantly, what kind of developers are more likely to participate? Such open questions remain to be explored. To this end, in this paper, we present the results of an empirical study conducted to investigate what motivates software developers to participate in CCSD and what inhibits software developers to participate in such competitions. An online questionnaire is sent out to more than 300 crowdsource software participants, of which 113 return valid responses. It is also sent to more than 150 industry practitioners, of which 75 return valid responses. The results suggest that the monetary rewards are not significantly important to motivate software developers to participate in CCSD. Instead, learning, social contacts, and peer recognition are more important. Besides the survey, we also analyze the historical data collected from one of the most popular software crowdsourcing platforms. The analysis results reveal that the Pareto principle holds for CCSD as well, and 0.9% of the participants win 86% competitions. The results support the premise that CCSD market is still at an early stage. Most of the professional software engineers do not participate seriously in crowdsourcing software development. Therefore, many crowdsourced tasks, especially complex tasks, may fail to receive any satisfying submission. These findings are worthwhile for the crowdsourcing platforms and companies who want to outsource their software development tasks to the CCSD platforms.