No abstract

Bellwether effect refers to the existence of exemplary projects (called the Bellwether) within a historical dataset to be used for improved prediction performance. Recent studies have shown an implicit assumption of using recently completed projects (referred to as moving window) for improved prediction accuracy. In this paper, we investigate the Bellwether effect on software effort estimation accuracy using moving windows. The existence of the Bellwether was empirically proven based on six postulations. We apply statistical stratification and Markov chain methodology to select the Bellwether moving window. The resulting Bellwether moving window is used to predict the software effort of a new project. Empirical results show that Bellwether effect exist in chronological datasets with a set of exemplary and recently completed projects representing the Bellwether moving window. Result from this study has shown that the use of Bellwether moving window with the Gaussian weighting function significantly improve the prediction accuracy.

Context: In addressing how best to estimate how much effort is required to develop software, a recent study found that using exemplary and recently completed projects [forming Bellwether moving windows (BMW)] in software effort prediction (SEP) models leads to relatively improved accuracy. More studies need to be conducted to determine whether the BMW yields improved accuracy in general, since different sizing and aging parameters of the BMW are known to affect accuracy. Objective: To investigate the existence of exemplary projects (Bellwethers) with defined window size and age parameters, and whether their use in SEP improves prediction accuracy. Method: We empirically investigate the moving window assumption based on the theory that the prediction outcome of a future event depends on the outcomes of prior events. Sampling of Bellwethers was undertaken using three introduced Bellwether methods (SSPM, SysSam, and RandSam). The ergodic Markov chain was used to determine the stationarity of the Bell-wethers. Results: Empirical results show that 1) Bellwethers exist in SEP and 2) the BMW has an approximate size of 50 to 80 exemplary projects that should not be more than 2 years old relative to the new projects to be estimated. Conclusion: The study's results add further weight to the recommended use of Bellwethers for improved prediction accuracy in SEP.

Abstract

Abstract-Software Testing Effort (STE), which contributes about 25-40% of the total development effort, plays a significant role in software development. In addressing the issues faced by companies in finding relevant datasets for STE estimation modeling prior to development, cross-company modeling could be leveraged. The study aims at assessing the effectiveness of cross-company (CC) and within-company (WC) projects in STE estimation. A robust multi-objective Mixed-Integer Linear Programming (MILP) optimization framework for the selection of CC and WC projects was constructed and estimation of STE was done using Deep Neural Networks. Results from our study indicate that the application of the MILP framework yielded similar results for both WC and CC modeling. The modeling framework will serve as a foundation to assist in STE estimation prior to the development of new a software project.

ObjectiveThere is little research that characterises knee pain related information disseminated via social media. However, variances in the content and quality of such sources could compromise optimal patient care. This study explored the nature of the comments on YouTube videos related to non-specific knee pain, to determine their helpfulness to the users.MethodsA systematic search identified 900 videos related to knee pain on the YouTube database. A total of 3537 comments from 58 videos were included in the study. A categorisation scheme was developed and 1000 randomly selected comments were analysed according to this scheme.ResultsThe most common category was the users providing personal information or describing a personal situation (19%), followed by appreciation or acknowledgement of others’ inputs (17%) and asking questions (15%). Of the questions, 33% were related to seeking help in relation to a specific situation. Over 10% of the comments contained negativity or disagreement; while 4.4% of comments reported they intended to pursue an action, based on the information presented in the video and/or from user comments.ConclusionIt was observed that individuals commenting on YouTube videos on knee pain were most often soliciting advice and information specific to their condition. The analysis of comments from the most commented videos using a keyword-based search approach suggests that the YouTube videos can be used for disseminating general advice on knee pain.

Context

End-users play an integral role in identifying requirements, validating software features' usefulness, locating defects, and in software product evolution in general. Their role in these activities is especially prominent in online application distribution platforms (OADPs), where software is developed for many potential users, and for which the traditional processes of requirements gathering and negotiation with a single group of end-users do not apply. With such vast access to end-users, however, comes the challenge of how to prioritize competing requirements in order to satisfy previously unknown user groups, especially with early releases of a product. One highly successful product that has managed to overcome this challenge is the Android Operating System (OS). While the requirements of early versions of the Android OS likely benefited from market research, new features in subsequent releases appear to have benefitted extensively from user reviews. Thus, lessons learned about how Android developers have managed to satisfy the user community over time could usefully inform other software products. We have used data mining and natural language processing (NLP) techniques to investigate the issues that were logged by the Android community, and how Google's remedial efforts correlated with users' requests. We found very strong alignment between end-users' top feature requests and Android developers' responses, particularly for the more recent Android releases. Our findings suggest that effort spent responding to end-users' loudest calls may be integral to software systems' survival, and a product's overall success.