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Effort estimation is a project management activity that is mandatory for the execution of software projects. Despite its importance, there have been just a few studies published on such activities within the Agile Global Software Development (AGSD) context. Their aggregated results were recently published as part of a secondary study that reported the state of the art on effort estimation in AGSD...
Developers choose identifiers to name entities during software coding. While these names are lexically restricted by the language, they reflect the understanding of the developer on the requirements that the entity is devoted for. In this paper, we analyze the use of such vocabularies to identify experts on code entities. For a real software development, e-Pol (Management Information System for Federal...
Number of defects remaining in a system provides an insight into the quality of the system. Defect detection systems predict defects by using software metrics and data mining techniques. Clustering analysis is adopted to build the software defect prediction models. Cluster ensembles have emerged as a prominent method for improving robustness, stability and accuracy of clustering solutions. The clustering...
In the software development, defects affect quality and cost in an adverse way. Therefore, various studies have been proposed defect prediction techniques. Most of current defect prediction approaches use past project data for building prediction models. That is, these approaches are difficult to apply new development projects without past data. In this study, we focus on the cross project prediction...
Development of software change prediction models, based on the change histories of a software, are valuable for early identification of change prone classes. Classification of these change prone classes is vital to yield competent use of limited resources in an organization. This paper validates Artificial Immune System (AIS) algorithms for development of change prediction models using six open source...
Refactorings are behavior-preserving source code transformations. While tool support exists for (semi) automatically identifying refactoring solutions, applying or not a recommended refactoring is usually up to the software developers, who have to assess the impact that the transformation will have on their system. Evaluating the pros (e.g., the bad smell removal) and cons (e.g., side effects of the...
Approaches to detect fault-prone modules have been studied for a long time. As one of these approaches, we proposed a technique using a text filtering technique. We assume that bugs relate to words and context that are contained in a software module. Our technique treats a module as text information. Based on the dictionary which was learned by classifying modules which induce bugs, the bug inducing...
Existing defect prediction models use product or process metrics and machine learning methods to identify defect-prone source code entities. Different classifiers (e.g., linear regression, logistic regression, or classification trees) have been investigated in the last decade. The results achieved so far are sometimes contrasting and do not show a clear winner. In this paper we present an empirical...
The Fish4Knowledge (F4K) project involves analysing video generated from multiple camera feeds to support environmental and ecological assessment. A workflow engine is utilised in the project which deals with on-demand user queries and batch queries, selection of a suitable computing platform on which to enact the workflow along with a selection of suitable software modules to use to support analysis...
Software prediction unveils itself as a difficult but important task which can aid the manager on decision making, possibly allowing for time and resources sparing, achieving higher software quality among other benefits. Bayesian Networks are one of the machine learning techniques proposed to perform this task. However, the data pre-processing procedures related to their application remain scarcely...
Reliable empirical models such as those used in software effort estimation or defect prediction are inherently dependent on the data from which they are built. As demands for process and product improvement continue to grow, the quality of the data used in measurement and prediction systems warrants increasingly close scrutiny. In this paper we propose a taxonomy of data quality challenges in empirical...
Performance problems pose a significant risk to software vendors. If left undetected, they can lead to lost customers, increased operational costs, and damaged reputation. Despite all efforts, software engineers cannot fully prevent performance problems being introduced into an application. Detecting and resolving such problems as early as possible with minimal effort is still an open challenge in...
This paper reports and discusses the results of an assessment study, which aimed to determine the extent to which the voting ensemble model offers reliable and improved estimation accuracy over five individual models (MLP, RBF, RT, KNN and SVR) in estimating software development effort. Five datasets were used for this purpose. The results confirm that individual models are not reliable as their performance...
Aspect mining investigates effective ways of finding crosscutting concerns in existing non-aspect oriented software. These crosscutting concerns can then be refactored into aspects to reduce the system's complexity and make it easier to understand, maintain, and evolve. There have been numerous studies introducing different aspect mining techniques, but they used different quality measures to evaluate...
Cross-project defect prediction is very appealing because (i) it allows predicting defects in projects for which the availability of data is limited, and (ii) it allows producing generalizable prediction models. However, existing research suggests that cross-project prediction is particularly challenging and, due to heterogeneity of projects, prediction accuracy is not always very good. This paper...
Scrum Teams use lightweight tools like Story Points, the Burn down chart, and Team Velocity. While essential, these tools alone provide insufficient information to maintain a high energy state that yields Hyper productivity. More data is required, but data collection itself can slow Teams. This effect must be avoided when productivity is the primary marker of success. Here we describe nine metrics...
In this paper, we empirically investigate the re-lationship of existing class level object-oriented metrics with fault proneness over the multiple releases of the software. Here we first, evaluate each metric for their potential to predict faults independently by performing univariate logistic regression analysis. Next, we perform cross-correlation analysis between the significant metrics to find...
Defects in every software must be handled properly, and the number of defects directly reflects the quality of a software. In recent years, researchers have applied data mining and machine learning methods to predicting software defects. However, in their studies, the method in which the machine learning models are directly adopted may not be precise enough. Optimizing the machine learning models...
This paper proposes a method to support Personal Software Process (PSP) in a development organization by classify the operations on a computer into a purpose of the user. PSP requires the developers to record and analyze their activity during the development process. There are several methods and systems to support the PSP, they records the operations automatically and also records a purpose of the...
Software fault prediction techniques are helpful in developing dependable software. In this paper, we proposed a novel framework that integrates testing and prediction process for unit testing prediction. Because high fault prone metrical data are much scattered and multi-centers can represent the whole dataset better, we used artificial immune network (aiNet) algorithm to extract and simplify data...
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