Software is a unique entity that has laid a strong impact on all other fields either related or not related to software. These include medical, scientific, business, educational, defence, transport, telecommunication to name a few. State-of-the-art professional domain activities demands the development of high quality software. High quality software attributes to a defect-free product, which is competent of producing predictable results and remains deliverable within time and cost constraints. It should be manageable with minimum interferences. It should also be maintainable, dependable, understandable and efficient. Thus, a systematic approach towards high quality software development is required due to increased competitiveness in today's business world, technological advances, hardware complexity and frequently changing business requirements.
Advancement in fundamental engineering aspects of software development enables IT enterprises to develop a more cost effective and better quality product through aptly organized defect management strategies. Inspection continues to be the most effective and efficient technique of defect management. To have an appropriate measurement of the inspection process, the process metric, Depth of Inspection (DI) and the people metric, Inspection Performance Metric (IPM) are introduced. The introduction of these pair of metrics can yield valuable information from a company in relation to the inspection …
IT industry should inculcate effective defect management on a continual basis to deploy nearly a zerodefect product to their customers. Inspection is one of the most imperative and effective strategies of defect management. Nevertheless, existing defect management strategies in leading software industries are successful to deliver a maximum of 96% defect-free product. An empirical study of various projects across several service-based and product-based industries proves the above affirmations. This paper provides an enhanced approach of inspection through a Four-Step Approach Model of Inspecti…
Software defect prediction is an important aspect of preventive maintenance of a software. Many techniques have been employed to improve software quality through defect prediction. This paper introduces an approach of defect prediction through a machine learning algorithm, support vector machines (SVM), by using the code smells as the factor. Smell prediction model based on support vector machines was used to predict defects in the subsequent releases of the eclipse software. The results signify the role of smells in predicting the defects of a software. The results can further be used as a ba…
In current research, there are contrasting results about the applicability of software source code metrics as features for defect prediction models. The goal of the paper is to evaluate the adoption of software metrics in models for software defect prediction, identifying the impact of individual source code metrics. With an empirical study on 275 release versions of 39 Java projects mined from GitHub, we compute 12 software metrics and collect software defect information. We train and compare three defect classification models. The results across all projects indicate that Decision Tree (DT) …
Defect prediction can be a powerful tool to guide the use of quality assurance resources. However, while lots of research covered methods for defect prediction as well as methodological aspects of defect prediction research, the actual cost saving potential of defect prediction is still unclear. Within this article, we close this research gap and formulate a cost model for software defect prediction. We derive mathematically provable boundary conditions that must be fulfilled by defect prediction models such that there is a positive profit when the defect prediction model is used. Our cost mod…
In recent years, defect prediction has received a great deal of attention in the empirical software engineering world. Predicting software defects before the maintenance phase is very important not only to decrease the maintenance costs but also increase the overall quality of a software product. There are different types of product, process, and developer based software metrics proposed so far to measure the defectiveness of a software system. This paper suggests to use a novel set of software metrics which are based on the similarities detected among the source code files in a software proje…
Companies that develop software have a pressure from customers to deliver better solutions, and to deliver solutions faster and cheaper. Many researchers have worked with suggestions on how to improve the development process; software process improvement. As software development is a very knowledge intensive task, both researchers and industry have recently turned their attention to knowledge management as a means to improve software development. This often involves developing technical tools, which many companies have spent resources on. But the tools are often not used in practise by develop…
Data originating from open-source software projects provide valuable information to enhance software quality. In the scope of Software Defect Prediction, one of the most challenging parts is extracting valid data about failure-prone software components from these repositories, which can help develop more robust software. In particular, collecting data, calculating metrics, and synthesizing results from these repositories is a tedious and error-prone task, which often requires understanding the programming languages involved in the mined repositories, eventually leading to a proliferation of la…
Defect prevention is the most vital but habitually neglected facet of software quality assurance in any project. If functional at all stages of software development, it can condense the time, overheads and wherewithal entailed to engineer a high quality product. The key challenge of an IT industry is to engineer a software product with minimum post deployment defects. This effort is an analysis based on data obtained for five selected projects from leading software companies of varying software production competence. The main aim of this paper is to provide information on various methods and p…
Large organizations have diverse product offerings to meet various business needs. To increase revenue, its common these days to offer software products as integrated product suite(s) rather than individual products. Creating and maintaining high quality software products within the integrated product suite requires rigorous product engineering methods. The sheer size of products and dependencies involved tend to raise unidentified defects that may become critical post product upgrades or after every release cycle. It is difficult to track such defects and its widespread across underlying sub-…
A set of software metrics for the evaluation of power management systems (PMSs) is presented. Such systems for managing power need to be autonomous, scalable, low in complexity, and comprised of portable algorithms in order to be well applied across the varying implementations that utilize power systems. Although similar metrics exist for software in general, their definitions do not readily lend themselves to the unique characteristics of power management systems or systems of similar architecture.
Explaining the prediction results of software defect prediction models is a challenging while practical task, which can provide useful information for developers to understand and fix the predicted bugs. To address this issue, recently, Jiarpakdee et al. proposed to use {two state-of-the-art} model-agnostic techniques (i.e., LIME and BreakDown) to explain the prediction results of bug prediction models. Their experiments show these tools can generate promising results and the generated explanations can assist developers understand the prediction results. However, the fact that LIME and BreakDo…
In a critical software system, the testers have to spend an enormous amount of time and effort to maintain the software due to the continuous occurrence of defects. Among such defects, some severe defects may adversely affect the software. To reduce the time and effort of a tester, many machine learning models have been proposed in the literature, which use the documented defect reports to automatically predict the severity of the defective software modules. In contrast to the traditional approaches, in this work we propose a metric-based software defect severity prediction (SDSP) model that u…
Software engineering is continuously facing the challenges of growing complexity of software packages and increased level of data on defects and drawbacks from software production process. This makes a clarion call for inventions and methods which can enable a more reusable, reliable, easily maintainable and high quality software systems with deeper control on software generation process. Quality and productivity are indeed the two most important parameters for controlling any industrial process. Implementation of a successful control system requires some means of measurement. Software metrics…
Context: The Importance of Dynamic Variability Management in Dynamic Software Product Lines. Objective: Define a protocol for conducting a systematic mapping study to summarize and synthesize evidence on dynamic variability management for Dynamic Software Product Lines in self-adaptive systems. Method: Application the protocol to conduct a systematic mapping study according the guidelines of K. Petersen. Results: A validated protocol to conduct a systematic mapping study. Conclusions: First findings show that it is necessary to visualize new ways to manage variability in dynamic software produ…
Nowadays, Software Process Improvement popularly known as SPI has been able to receive an immense concern in the continuous process to purify software quality. Several Agile methodologies previously have worked with Extreme programming (XP). Before improving the process, defect prevention (DP) is inevitable. In addition, DP largely depends on defect detection either found earlier in the design and implementation stages or held in the testing phases. However, testing maturity model integration (TMMI) has a crucial aspect in DP as well as process improvement of the software. In particular, when …
Machine Learning approaches are good in solving problems that have less information. In most cases, the software domain problems characterize as a process of learning that depend on the various circumstances and changes accordingly. A predictive model is constructed by using machine learning approaches and classified them into defective and non-defective modules. Machine learning techniques help developers to retrieve useful information after the classification and enable them to analyse data from different perspectives. Machine learning techniques are proven to be useful in terms of software …
Project Management process plays a significant role in effective development of software projects. Key challenges in the project management process are the estimation of time, cost, defect count, and subsequently selection of apt developers. Therefore precise estimation of above stated factors decides the success level of a project. This paper provides an empirical study of several projects developed in a service oriented software company in order to comprehend the project management process. The analysis throws light on the existence of variation in the aforementioned factors between estimati…
Software design is one of the most important and key activities in the system development life cycle (SDLC) phase that ensures the quality of software. Different key areas of design are very vital to be taken into consideration while designing software. Software design describes how the software system is decomposed and managed in smaller components. Object-oriented (OO) paradigm has facilitated software industry with more reliable and manageable software and its design. The quality of the software design can be measured through different metrics such as Chidamber and Kemerer (CK) design metri…
How risks are managed implicitly and explicitly at multiple levels of agile projects has not been extensively studied and there is a need to investigate how risk management can be used in large agile projects. This is the objective of this exploratory study which investigates the following research question: How does a large software/hardware development project using agile practices manage uncertainty at project/subproject and work package levels?
The need for change in project requirements is necessary for every organization due to change in technology,change in government policy,and change of customer or stakeholders requirements.Requirement Change Management RCM is not an easy task,especially in Global Software Development GSD where team members are globally distributed in different geographical location and a cultural difference is present between team members. So it becomes more difficult to manage these changes. There are a number of risks that are faced during requirement change management in global software development process. …
Defect estimation and prediction are some of the main modulating factors for the success of software projects in any software industry. Maturity and competency of a project manager in efficient prediction and estimation of resource capabilities are one of the strategic driving forces towards the generation of high quality software. Currently, there are no estimation techniques developed through empirical analysis to evaluate the decision capability of a project manager towards resource allocation for effective defect management. This paper brings out an empirical study carried out in a product…
This paper reports on our definition of guidelines for managing global software development (GSD) that implements the specific practice - manage requirements changes - of the Capability Maturity Model Integration (CMMI) Level 2. The guidelines present a model for change management and traceability that supports the implementation of the specific CMMI Level 2 goal. Also, to support the effective management of the system engineering processes, an adaptation of the Project Management Body of Knowledge (PMBOK) process group (PG) for project life-cycle practices is provided. We introduce a cloud-ba…
Optimizing the quality of software is a function of the degree of reviews made during the early life of a software development process. Reviews detect errors and potential errors early in the software development process. The errors detected during the early life cycle of software are least expensive to correct. Efficient involvement in software inspections and technical reviews, help developers improve their own skills, thereby mitigating the occurrence of errors in the later stage of software development process. The ideas gathered on this paper point that a properly implemented program of t…
Today, most automated test generators, such as search-based software testing (SBST) techniques focus on achieving high code coverage. However, high code coverage is not sufficient to maximise the number of bugs found, especially when given a limited testing budget. In this paper, we propose an automated test generation technique that is also guided by the estimated degree of defectiveness of the source code. Parts of the code that are likely to be more defective receive more testing budget than the less defective parts. To measure the degree of defectiveness, we leverage Schwa, a notable defec…
Software defect prediction is an essential task during the software development Lifecycle as it can help managers to identify the most defect-proneness modules. Thus, it can reduce the test cost and assign testing resources efficiently. Many classification methods can be used to determine if the software is defective or not. Support Vector Machine (SVM) has not been used extensively for such problems because of its instability when applied on different datasets and parameter settings. The main parameter that influences the accuracy is the choice of the kernel function. The use of kernel functi…
DBBRBF- Convalesce optimization for software defect prediction problem using hybrid distribution base balance instance selection and radial basis Function classifier
Service Oriented Architecture A Revolution for Project Management Software has changed the way projects today are moving on the fly with the help of web services booming the industry. Service oriented architecture improves performance and the communication between the distributed and remote teams. Web Services to Provide Project Management software the visibility and control of the application development lifecycle-giving a better control over the entire development process, from the management stage through development. The goal of Service Oriented Architecture for Project Management Software…
The Software Design Document of UUIS describes the prototype design details of the system architecture, database layer, deployment and configuration details as well as test cases produced while working the design and implementation of the prototype. The requirements specification of UUIS are detailed in arXiv:1005.0783.
In the past 20 years, defect prediction studies have generally acknowledged the effect of class size on software prediction performance. To quantify the relationship between object-oriented (OO) metrics and defects, modelling has to take into account the direct, and potentially indirect, effects of class size on defects. However, some studies have shown that size cannot be simply controlled or ignored, when building prediction models. As such, there remains a question whether, and when, to control for class size. This study provides a new in-depth examination of the impact of class size on the…
Various project management approaches do not consider the impact that uncertainties have on the project. The identified threats by uncertainty in a projec day-to-day are real and immediate and the expectations in a project are often high. The project manager faces a dilemma: decisions must be made in the present about future situations which are inherently uncertain. The use of uncertainty management in project can be a determining factor for the project success. This paper presents a systematic review about uncertainties management in software projects and a guide is proposed based on the rev…
Software quality is one of the essential aspects of a software. With increasing demand, software designs are becoming more complex, increasing the probability of software defects. Testers improve the quality of software by fixing defects. Hence the analysis of defects significantly improves software quality. The complexity of software also results in a higher number of defects, and thus manual detection can become a very time-consuming process. This gave researchers incentives to develop techniques for automatic software defects detection. In this paper, we try to analyze the state of the art …
Context: Software industry is continuously exploring better ways to develop applications. A new phenomenon to achieve this is Cloud based Global Software Development (CGSD), which refers to the adoption of cloud computing services by organizations to support global software development projects. The CGSD approach affects the strategic and operational aspects of the way projects are managed. Objective: The objective of the study is to identify the success factors which contribute to management of CGSD projects. Methods: We carried out a Multivocal Literature Review (MLR) to identify the success…
Defects are common in software systems and can potentially cause various problems to software users. Different methods have been developed to quickly predict the most likely locations of defects in large code bases. Most of them focus on designing features (e.g. complexity metrics) that correlate with potentially defective code. Those approaches however do not sufficiently capture the syntax and different levels of semantics of source code, an important capability for building accurate prediction models. In this paper, we develop a novel prediction model which is capable of automatically learn…
One truism of deep learning is that the automatic feature engineering (seen in the first layers of those networks) excuses data scientists from performing tedious manual feature engineering prior to running DL. For the specific case of deep learning for defect prediction, we show that that truism is false. Specifically, when we preprocess data with a novel oversampling technique called fuzzy sampling, as part of a larger pipeline called GHOST (Goal-oriented Hyper-parameter Optimization for Scalable Training), then we can do significantly better than the prior DL state of the art in 14/20 defec…
Defect Prevention is the most critical but most neglected component of the software quality assurance in any project. If applied at all stages of software development, it can reduce the time, cost and resources required to engineer a high quality product. Software inspection has proved to be the most effective and efficient technique enabling defect detection and prevention. Inspections carried at all phases of software life cycle have proved to be most beneficial and value added to the attributes of the software. Work is an analysis based on the data collected for three different projects fro…
Duplicated code or code clones are a kind of code smell that have both positive and negative impacts on the development and maintenance of software systems. Software clone research in the past mostly focused on the detection and analysis of code clones, while research in recent years extends to the whole spectrum of clone management. In the last decade, three surveys appeared in the literature, which cover the detection, analysis, and evolutionary characteristics of code clones. This paper presents a comprehensive survey on the state of the art in clone management, with in-depth investigation …
Context: There is considerable diversity in the range and design of computational experiments to assess classifiers for software defect prediction. This is particularly so, regarding the choice of classifier performance metrics. Unfortunately some widely used metrics are known to be biased, in particular F1. Objective: We want to understand the extent to which the widespread use of the F1 renders empirical results in software defect prediction unreliable. Method: We searched for defect prediction studies that report both F1 and the Matthews correlation coefficient (MCC). This enabled us to det…
Accurately predicting faulty software units helps practitioners target faulty units and prioritize their efforts to maintain software quality. Prior studies use machine-learning models to detect faulty software code. We revisit past studies and point out potential improvements. Our new study proposes a revised benchmarking configuration. The configuration considers many new dimensions, such as class distribution sampling, evaluation metrics, and testing procedures. The new study also includes new datasets and models. Our findings suggest that predictive accuracy is generally good. However, pre…
Context:Quality requirements (QRs) describe the desired quality of software, and they play an important role in the success of software projects. In agile software development (ASD), QRs are often ill-defined and not well addressed due to the focus on quickly delivering functionality. Rapid software development (RSD) approaches (e.g., continuous delivery and continuous deployment), which shorten delivery times, are more prone to neglect QRs. Despite the significance of QRs in both ASD and RSD, there is limited synthesized knowledge on their management in those approaches. Objective:This study …
It is no secret that many projects fail, regardless of the business sector, software projects are notoriously disaster victims, not necessarily because of technological failure, but more often due to their uncertainties. The threats identified by uncertainty in day-to-day of a project are real and immediate and the stakes in a project are often high. This paper presents a systematic review about software project management uncertainties. It helps to identify the difficulties and the actions that can minimize the uncertainties effects in the projects and how managers and teams can prepare thems…
A nearly autonomous management and control (NAMAC) system is designed to furnish recommendations to operators for achieving particular goals based on NAMAC's knowledge base. As a critical component in a NAMAC system, digital twins (DTs) are used to extract information from the knowledge base to support decision-making in reactor control and management during all modes of plant operations. With the advancement of artificial intelligence and data-driven methods, machine learning algorithms are used to build DTs of various functions in the NAMAC system. To evaluate the uncertainty of DTs and its …
Automated test generators, such as search based software testing (SBST) techniques, replace the tedious and expensive task of manually writing test cases. SBST techniques are effective at generating tests with high code coverage. However, is high code coverage sufficient to maximise the number of bugs found? We argue that SBST needs to be focused to search for test cases in defective areas rather in non-defective areas of the code in order to maximise the likelihood of discovering the bugs. Defect prediction algorithms give useful information about the bug-prone areas in software. Therefore, w…
Context: Software security patch management purports to support the process of patching known software security vulnerabilities. Given the increasing recognition of the importance of software security patch management, it is important and timely to systematically review and synthesise the relevant literature on this topic. Objective: This paper aims at systematically reviewing the state of the art of software security patch management to identify the socio-technical challenges in this regard, reported solutions (i.e., approaches, tools, and practices), the rigour of the evaluation and the indu…
