Search-based approaches for software development effort estimation

2011 - 2012Effort estimation is a critical activity for planning and monitoring software project development and for delivering the product on time and within budget. Significant over or under-estimates expose a software project to several risks. As a matter of fact under-estimates could lead to addition of manpower to a late software project, making the project later (Brooks’s Law), or to the cancellation of activities, such as documentation and testing, negatively impacting on software quality and maintainability. Thus, the competitiveness of a software company heavily depends on the ability of its project managers to accurately predict in advance the effort required to develop software system. However, several challenges exists in making accurate estimates, e.g., the estimation is needed early in the software lifecycle, when few information about the project are available, or several factors can impact on project effort and these factor are usually specific for different production contexts. Several techniques have been proposed in the literature to support project manager in estimating software project development effort. In the last years the use of Search-Based (SB) approaches has been suggested to be employed as an effort estimation technique. These approaches include a variety of meta-heuristics, such as local search techniques (e.g., Hill Climbing, Tabu Search, Simulated Annealing) or Evolutionary Algorithms (e.g., Genetic Algorithms, Genetic Programming). The idea underlying the use of such techniques is based on the reformulation of software engineering problems as search or optimization problems whose goal is to find the most appropriate solutions which conform to some adequacy criteria (i.e., problem goals). In particular, the use of SB approaches in the context of effort estimation is twofold: they can be exploited to build effort estimation models or to enhance the use of existing effort estimation techniques. The usage reported in the literature of SB approaches for effort estimation have provided promising results that encourage further investigations. However, they can be considered preliminary studies. As a matter of fact, the capabilities of these approaches were not fully exploited, either the employed empirical analyses did not consider the more recent recommendations on how to carry out this kind of empirical assessment in the effort estimation and in the SBSE contexts. The main aim of the PhD dissertation is to provide an insight on the use of SB techniques for the effort estimation trying to highlight strengths and weaknesses of these approaches for both the uses above mentioned. [edited by Author]XI n.s

Py2Cy: A Genetic Improvement Tool To Speed Up Python

Due to its ease of use and wide range of custom libraries, Python has quickly gained popularity and is used by a wide range of developers all over the world. While Python allows for fast writing of source code, the resulting programs are slow to execute when compared to programs written in other programming languages like C. One of the reasons for its slow execution time is the dynamic typing of variables. Cython is an extension to Python, which can achieve execution speed-ups by compiler optimization. One possibility for improvements is the use of static typing, which can be added to Python scripts by developers. To alleviate the need for manual effort, we create Py2Cy, a Genetic Improvement tool for automatically converting Python scripts to statically typed Cython scripts. To show the feasibility of improving runtime with Py2Cy, we optimize a Python script for generating Fibonacci numbers. The results show that Py2Cy is able to speed up the execution time by up to a factor of 18

MEG: Multi-objective Ensemble Generation for Software Defect Prediction

Background: Defect Prediction research aims at assisting software engineers in the early identification of software defect during the development process. A variety of automated approaches, ranging from traditional classification models to more sophisticated learning approaches, have been explored to this end. Among these, recent studies have proposed the use of ensemble prediction models (i.e., aggregation of multiple base classifiers) to build more robust defect prediction models. / Aims: In this paper, we introduce a novel approach based on multi-objective evolutionary search to automatically generate defect prediction ensembles. Our proposal is not only novel with respect to the more general area of evolutionary generation of ensembles, but it also advances the state-of-the-art in the use of ensemble in defect prediction. / Method: We assess the effectiveness of our approach, dubbed as Multi-objective Ensemble Generation (MEG), by empirically benchmarking it with respect to the most related proposals we found in the literature on defect prediction ensembles and on multi-objective evolutionary ensembles (which, to the best of our knowledge, had never been previously applied to tackle defect prediction). / Result: Our results show that MEG is able to generate ensembles which produce similar or more accurate predictions than those achieved by all the other approaches considered in 73% of the cases (with favourable large effect sizes in 80% of them). / Conclusions: MEG is not only able to generate ensembles that yield more accurate defect predictions with respect to the benchmarks considered, but it also does it automatically, thus relieving the engineers from the burden of manual design and experimentation

Agile Effort Estimation: Have We Solved the Problem Yet? Insights From A Replication Study

In the last decade, several studies have explored automated techniques to estimate the effort of agile software development. We perform a close replication and extension of a seminal work proposing the use of Deep Learning for Agile Effort Estimation (namely Deep-SE), which has set the state-of-the-art since. Specifically, we replicate three of the original research questions aiming at investigating the effectiveness of Deep-SE for both within-project and cross-project effort estimation. We benchmark Deep-SE against three baselines (i.e., Random, Mean and Median effort estimators) and a previously proposed method to estimate agile software project development effort (dubbed TF/IDF-SVM), as done in the original study. To this end, we use the data from the original study and an additional dataset of 31,960 issues mined from TAWOS, as using more data allows us to strengthen the confidence in the results, and to further mitigate external validity threats. The results of our replication show that Deep-SE outperforms the Median baseline estimator and TF/IDF-SVM in only very few cases with statistical significance (8/42 and 9/32 cases, respectively), thus confounding previous findings on the efficacy of Deep-SE. The two additional RQs revealed that neither augmenting the training set nor pre-training Deep-SE play lead to an improvement of its accuracy and convergence speed. These results suggest that using semantic similarity is not enough to differentiate user stories with respect to their story points; thus, future work has yet to explore and find new techniques and features that obtain accurate agile software development estimates

An Empirical Study on the Fairness of Pre-trained Word Embeddings

Pre-trained word embedding models are easily distributed and applied, as they alleviate users from the effort to train models themselves. With widely distributed models, it is important to ensure that they do not exhibit undesired behaviour, such as biases against population groups. For this purpose, we carry out an empirical study on evaluating the bias of 15 publicly available, pre-trained word embeddings model based on three training algorithms (GloVe, word2vec, and fastText) with regard to four bias metrics (WEAT, SEMBIAS, DIRECT BIAS, and ECT). The choice of word embedding models and bias metrics is motivated by a literature survey over 37 publications which quantified bias on pre-trained word embeddings. Our results indicate that fastText is the least biased model (in 8 out of 12 cases) and small vector lengths lead to a higher bias

How Do Android Developers Improve Non-Functional Properties of Software?

Nowadays there is an increased pressure on mobile app developers to take non-functional properties into account. An app that is too slow or uses much bandwidth will decrease user satisfaction, and thus can lead to users simply abandoning the app. Although automated software improvement techniques exist for traditional software, these are not as prevalent in the mobile domain. Moreover, it is yet unknown if the same software changes would be as effective. With that in mind, we mined overall 100 Android repositories to find out how developers improve execution time, memory consumption, bandwidth usage and frame rate of mobile apps. We categorised non-functional property (NFP) improving commits related to performance to see how existing automated software improvement techniques can be improved. Our results show that although NFP improving commits related to performance are rare, such improvements appear throughout the development lifecycle. We found altogether 560 NFP commits out of a total of 74,408 commits analysed. Memory consumption is sacrificed most often when improving execution time or bandwidth usage, although similar types of changes can improve multiple non-functional properties at once. Code deletion is the most frequently utilised strategy except for frame rate, where increase in concurrency is the dominant strategy. We find that automated software improvement techniques for mobile domain can benefit from addition of SQL query improvement, caching and asset manipulation. Moreover, we provide a classifier which can drastically reduce manual effort to analyse NFP improving commits

Linear programming as a baseline for software effort estimation

Software effort estimation studies still suffer from discordant empirical results (i.e., conclusion instability) mainly due to the lack of rigorous benchmarking methods. So far only one baseline model, namely, Automatically Transformed Linear Model (ATLM), has been proposed yet it has not been extensively assessed. In this article, we propose a novel method based on Linear Programming (dubbed as Linear Programming for Effort Estimation, LP4EE) and carry out a thorough empirical study to evaluate the effectiveness of both LP4EE and ATLM for benchmarking widely used effort estimation techniques. The results of our study confirm the need to benchmark every other proposal against accurate and robust baselines. They also reveal that LP4EE is more accurate than ATLM for 17% of the experiments and more robust than ATLM against different data splits and cross-validation methods for 44% of the cases. These results suggest that using LP4EE as a baseline can help reduce conclusion instability. We make publicly available an open-source implementation of LP4EE in order to facilitate its adoption in future studies