Background: Software migration across programming languages is a critical yet labor-intensive task, often requiring deep code understanding and manual intervention. Aims: In this study, we aim to develop a fully automated agent for end-to-end code translation and validation. Method: First, we generate code comments from Java source code using various large language models (LLMs) to enhance code comprehension and facilitate cross-language translation. Second, leveraging these AI-generated comments, we automatically generate equivalent C# code, demonstrating the potential of AI in software migration and interoperability. Third, we complete both Java and generated C# code and prepare them to execute. Fourth, we apply automated unit testing to assess functional correctness and ensure the reliability of AI-generated code. Results: Our results show that a fully automated LLM agent may effectively bridge programming languages with minimal human input. This approach opens new possibilities for scalable, AIdriven software modernization and cross-platform development. Conclusions: We recommend that such an LLM agent should be used to support human experts during the generation of reliable and correct code.

In requirements engineering (RE), anaphoric ambiguity is a frequent cause of misunderstandings. It can have a detrimental effect on the quality of requirements and jeopardize the success of a project. If stakeholders of the system, such as testers, developers, or customers, have different understandings or interpretations of software requirements, the system may not be accepted during customer validation. Despite its significance, there has been limited investigation into anaphoric ambiguity in RE. However, focusing on both recognizing and solving uncertainty can be more advantageous than just identifying it. Therefore we investigated the effectiveness of various QA learning techniques including encoder-based and text generation-based NLP models for two goals. We conduct detailed numerical experiments using various transformer models on two public requirements datasets and one generic dataset. Our results indicated that our QA architecture exhibits superior performance compared to baseline models in detecting ambiguity as well as resolving anaphora in contrast to other baseline approaches. We showed that our developed architecture can automatically support requirement development to minimize interpretation risk between stakeholders.

Bug triaging is a critical task in any software development project. It entails triagers going over a list of open bugs, deciding whether each is required to be addressed, and, if so, which developer should fix it. However, the manual bug assignment in Issue Tracking Systems (ITS) offers only a limited solution and might easily fail when triagers are required to handle a large number of bug reports. During the automated assignment, there are multiple sources of uncertainties in the ITS, which should be addressed meticulously. In this study, we develop a Markov decision process (MDP) model for an online bug triage problem. In addition to an optimization-based myopic technique, we provide an ADP-based bug triage solution, called ADPTriage, which has the ability to reflect the downstream uncertainty in the bug arrivals and developers’ timetables. Specifically, without placing any limits on the underlying stochastic process, this technique enables real-time decision-making on bug assignments while taking into consideration developers’ expertise, bug type, and bug fixing time. Our result shows a significant improvement over the myopic approach in terms of assignment accuracy and fixing time. We also demonstrate the empirical convergence of the model and conduct sensitivity analysis with various model parameters. Accordingly, this work constitutes a significant step forward in addressing the uncertainty in bug triage.

Abstract In this study, we compare the Bayesian personalized ranking (BPR) algorithms with two recent state‐of‐the‐art algorithms, namely, noisy‐label robust Bayesian point‐wise optimization (NBPO) and Light Graph Convolution Network (LightGCN) algorithms, to validate and generalize their performance by using six publicly available datasets and one proprietary dataset containing web‐based data visualization usage records. We follow the guidelines explained in the original studies to pre‐process the input data and evaluate these algorithms using various evaluation metrics. We also perform hyperparameter tuning for the recommendation algorithms to determine the optimal configuration resulting in the best recommendation quality. We observe that the best hyperparameter configuration varies based on the algorithms and the datasets. The results of our analysis show some similarities with the results of the original studies while differing in certain respects. We observe that adaptive oversampling BPR (AOBPR) and LightGCN algorithms generate higher quality recommendations than the other algorithms. However, algorithm convergence rates vary significantly for each dataset. We note that the AOBPR approach is particularly useful for data visualization recommendation task, and can contribute to the improved recommendations in practice.

In software engineering practice, fixing a bug promptly reduces the associated costs. On the other hand, the manual bug fixing process can be time-consuming, cumbersome, and error-prone. In this work, we introduce a bug triaging method, called Dependency-aware Bug Triaging (DABT), which leverages natural language processing and integer programming to assign bugs to appropriate developers. Unlike previous works that mainly focus on one aspect of the bug reports, DABT considers the textual information, cost associated with each bug, and dependency among them. Therefore, this comprehensive formulation covers the most important aspect of the previous works while considering the blocking effect of the bugs. We report the performance of the algorithm on three open-source software systems, i.e., EclipseJDT, LibreOffice, and Mozilla. Our result shows that DABT is able to reduce the number of overdue bugs up to 12%. It also decreases the average fixing time of the bugs by half. Moreover, it reduces the complexity of the bug dependency graph by prioritizing blocking bugs.

Forecasting stock price movements is an important task for investors and traders, though still very difficult due to the unstable nature and complex behavior of the stock market. Accordingly, each piece of information related to stock prices can be deemed useful. In this regard, social media platforms provide a vast amount of information that can be used to predict stock movements. In this study, we compare the performances of various traditional, deep learning, and state-of-art pre-trained transformer models for text classification of tweets related to the stock market, which are obtained through a financial microblog, StockTwits. For this purpose, we collected 100,000 labeled messages of five stocks, namely, Apple Inc. (AAPL), Amazon (AMZN), Boeing Co, (BA), Walt Disney Co. (DIS), and the SPDR S&P 500 ETF Trust (SPY) for a period between December 2019 and June 2020. We used logistic regression and random forest as traditional classifiers and Long Short Term Memory and Gated Recurrent Unit as the deep learning algorithms, and BERT, DistillBERT, RoBERTa, and XLNet as the state-of-art transformer models to classify tweets as either “bearish” or “bullish”. Our numerical study showed that RoBERTa outperformed traditional classifiers and deep learning algorithms in terms of average F1-scores.

AIM: Having a reliable prediction model of radio signal strength is an essential tool for planning and designing a radio network. Given a geographic region, and associated power estimates linked to the transmitter placements, our objective is to develop machine learning models to predict the strength of the radio signals. BACKGROUND: The propagation model is often used to determine the optimal location of radio transmitters in order to optimize the power coverage in a geographic area of interest. However, it is often a costly operation to obtain the exact power measurements over a region for a given set of transmitter locations. Therefore, fast prediction methods are needed to estimate the power values given limited data. METHODOLOGY: We consider a dataset consisting of simulated power at each point in an environment for a given set of transmitter locations. We experiment with various machine learning models, namely, generalized linear models (GLMs), neural networks (NNs), and k-nearest neighbor (KNN), to estimate the power values for a given transmitter placement. We investigate various feature engineering approaches to enhance the predictive performance of the machine learning models. RESULTS: We observe that employed feature engineering methods such as polynomial degrees and transmitter to cluster distances significantly improve the prediction accuracy. In particular, GLM model performance notably improves thanks to these extracted features, where mean absolute error (MAE) is reduced around 77% from 11.37 dB to 2.55 dB. We note that KNN with k = 2 and DNN models perform better than NN and GLM. KNN has the best performance with an average MAE of 0.65dB and also substantially faster to train than NN/DNN models. In addition, our analysis shows that, to train a well-performing machine learning model, it is sufficient to use a dataset consisting of measurements at a fraction of the potential transmitter locations in a given region. CONCLUSIONS: Machine learning methods are highly effective for the coverage prediction task. Using carefully engineered features, simple models such as GLMs and KNNs can be as effective as more complex ones, especially for small datasets.

Sentiment analysis of financial news and social media messages along with movement of stock prices could aid in improving the forecasting accuracy of stock prices. In this regard, we aim to perform sentiment analysis of a financial microblog, namely, StockTwits. We carried out the analysis on labelled messages of twelve stocks for a period of five months ranging from May 2019 to September 2019 using various Deep Learning (DL) approaches. We compared the performance of the DL classifiers with traditional machine learning approaches. Long Short Term Memory (LSTM) model and its variations such as bidirectional LSTM and bidrirectional LSTM with dropout outperformed other classifiers. Though use of dropout mechanism did not improve the performance of the model but there was a decrease in bias and variance. Further, we evaluated the performance of various optimizers such as rmsprop, adam, adagrad, adamax and nadam on LSTM. The success rate of all optimizers was similar.

In this study, filtered network approaches such as Minimum Spanning Tree and Planar Maximally Filtered Graphs, are used to analyse the topological structure of constituents of S&P Toronto Stock Exchange Composite Index for a period of three years from January 1, 2015 till Decemeber 31, 2017. For this purpose, rolling correlation for each pair of stocks was calculated for six different time windows of 1, 2, 3, 4, 6 and 12 months. Based on the topological structure, the stocks were categorized into core and peripheral stocks using network measures such as degree centrality, betweenness centrality, eccentricity and eigenvector centrality. Categorization of stocks into core and peripheral was consistent for both MST and PMFG based networks for all time windows. Financial stocks were found to be core stocks. Topological structure helps to understand the inter-relationship among the stocks. It would aid in interpreting the nature of economic factors affecting similar group of stocks. Identification and categorisation of core and peripheral stocks could be used as a base for construction of portfolios and risk management.

Complex dynamics of stock market could be attributed to various factors ranging from company's financial ratios to investors' sentiment and reaction to Financial news. The paper aims to classify Financial news articles as "hot" (significant) and "non-hot" (non-significant). The study is carried out using Dow Jones newswires text feed for a period of four years spanning from 2013 till 2017. Bag-of-ngrams appraoch and Term Frequency-Inverse Document Frequency (TF-IDF) were used for text representation and text weighting, respectively. Four linear classifiers, namely, Logistic Regression (LR), Support Vector Machine (SVM), k Nearest Neighbours (kNN) and multinomial Naïve Bayes (mNB) were used. Grid search was used for hyperparameter optimisation. Performance of the classifiers was evaluated using five measures, namely, success rate, precision, recall, F1 measure and area under receiver operating characteristics curve. LR and SVM outperformed other models in terms of all five performance measures for both Bag-of-ngrams model and Bag-of-ngrams model with TF-IDF approach. Use of TF-IDF improved performance of the classifiers, especially, in case of mNB. This study serves as a stepping stone in identification of important/relevant news, which could used as predictors for stock price forecasting.

A significant amount of time is spent by software developers in investigating bug reports. It is useful to indicate when a bug report will be closed, since it would help software teams to prioritise their work. Several studies have been conducted to address this problem in the past decade. Most of these studies have used the frequency of occurrence of certain developer activities as input attributes in building their prediction models. However, these approaches tend to ignore the temporal nature of the occurrence of these activities. In this paper, a novel approach using Hidden Markov models (HMMs) and temporal sequences of developer activities is proposed. The approach is empirically demonstrated in a case study using eight years of bug reports collected from the Firefox project. We provide additional details below. In a software bug repository, recorded developer activities occur sequentially. For example, activity C (a certain person has been copied on the bug report) is followed by activity A (bug confirmed and assigned to a named developer), which in turn is followed by activity Z (bug reached status resolved). Additional piece of information is developers' level of expertise, such as novice (N), intermediate (M), or experienced (E), at the time of report creation. We combine these data together to produce a sequence of temporal activities associated with bug reports in the Firefox bug repository.

In this study, we investigate the effect of EC on the defect-proneness of large industrial software systems and explain why the effects vary.

Abstract Information technology consumes up to 10% of the world's electricity generation, contributing to CO2emissions and high energy costs. Data centers, particularly databases, use up to 23% of this energy. Therefore, building an energy‐efficient (green) database engine could reduce energy consumption and CO2emissions. The goal of this study is to understand the factors driving databases' energy consumption and execution time throughout their evolution. We conducted an empirical case study of energy consumption by 2 MySQL database engines, InnoDB and MyISAM, across 40 releases. We examined the relationships of 4 software metrics to energy consumption and execution time to determine which metrics reflect the greenness and performance of a database. Our analysis shows that database engines' energy consumption and execution time increase as databases evolve. Moreover, the lines of code (LOC) metric is correlated moderately to strongly with energy consumption and execution time in 88% of cases. Our findings provide insights to practitioners and researchers. Database administrators may use them to select a fast, green release of the MySQL database engine. MySQL developers may use LOC to assess products' greenness and performance. Researchers may use our findings to further develop new hypotheses or build models predicting greenness and performance of databases.

A significant amount of time is spent by software developers in investigating bug reports. It is useful to indicate when a bug report will be closed, since it would help software teams to prioritise their work. Several studies have been conducted to address this problem in the past decade. Most of these studies have used the frequency of occurrence of certain developer activities as input attributes in building their prediction models. However, these approaches tend to ignore the temporal nature of the occurrence of these activities. In this paper, a novel approach using Hidden Markov Models and temporal sequences of developer activities is proposed. The approach is empirically demonstrated in a case study using eight years of bug reports collected from the Firefox project. Our proposed model correctly identifies bug reports with expected bug fix times. We also compared our proposed approach with the state of the art technique in the literature in the context of our case study. Our approach results in approximately 33 percent higher F-measure than the contemporary technique based on the Firefox project data.

The same defect can be rediscovered by multiple clients, causing unplanned outages and leading to reduced customer satisfaction. In the case of popular open source software, high volume of defects is reported on a regular basis. A large number of these reports are actually duplicates / rediscoveries of each other. Researchers have analyzed the factors related to the content of duplicate defect reports in the past. However, some of the other potentially important factors, such as the inter-relationships among duplicate defect reports, are not readily available in defect tracking systems such as Bugzilla. This information may speed up bug fixing, enable efficient triaging, improve customer profiles, etc. In this paper, we present three defect rediscovery datasets mined from Bugzilla. The datasets capture data for three groups of open source software projects: Apache, Eclipse, and KDE. The datasets contain information about approximately 914 thousands of defect reports over a period of 18 years (1999-2017) to capture the inter-relationships among duplicate defects. We believe that sharing these data with the community will help researchers and practitioners to better understand the nature of defect rediscovery and enhance the analysis of defect reports.

Background. Stability and growth in life insurance market is an important economic indicator. Therefore, yearly coverage lapse-rate estimates are one of the key statistics that actuarial analysts need to characterize and manage the insurance business. Aim. We aim to present machine learning based approach applied to a real data set covering a decade long customer history of an international financial services corporation. Methodology. A challenging issue with this problem is evolving nature of the lapse determinants. We present diagnostic analysis characterizing the relationship among variables and their respective predictive value. Results. Particularly, a number of fixation variables are identified that cause over-fitting. Then a predictive modeling approach and validation results are presented to analyze the comparative strengths of different algorithms. Conclusion. Our results show that Random Forest performed better than logistic regression and naive Bayes in terms of specificity and sensitivity measures.

Abstract Evolutionary coupling (EC) is defined as the implicit relationship between 2 or more software artifacts that are frequently changed together. Changing software is widely reported to be defect‐prone. In this study, we investigate the effect of EC on the defect proneness of large industrial software systems and explain why the effects vary. We analysed 2 large industrial systems: a legacy financial system and a modern telecommunications system. We collected historical data for 7 years from 5 different software repositories containing 176 thousand files. We applied correlation and regression analysis to explore the relationship between EC and software defects, and we analysed defect types, size, and process metrics to explain different effects of EC on defects through correlation. Our results indicate that there is generally a positive correlation between EC and defects, but the correlation strength varies. Evolutionary coupling is less likely to have a relationship to software defects for parts of the software with fewer files and where fewer developers contributed. Evolutionary coupling measures showed higher correlation with some types of defects (based on root causes) such as code implementation and acceptance criteria. Although EC measures may be useful to explain defects, the explanatory power of such measures depends on defect types, size, and process metrics.

Context: Software code review, as an engineering best practice, refers to the inspection of the code change in order to find possible defects and ensure change quality. Code reviews, however, may not guarantee finding the defects. Thus, there is a risk for a defective code change in a given patch, to pass the review process and be submitted.

Software development organizations often need to balance productivity and sustainable profitability. To keep a balance, attention needs to be given as to where and when defects are re-assigned. This paper reports a replicated experiment comparing the prediction differences of re-assigned defects between proprietary and open-source software (OSS) projects. We pursue a quantitative approach based on logistic regression that relies on two OSS defect tracking datasets: namely, Mozilla and Eclipse. Here, we focus our attention to the reputation of software developers and where it affects the re-assigned defects. We explore the correlation between reassigned defects and other related factors such as the reputation of the software developer. The replication of the original quantitative approach aims to verify the results of the original experiment on OSS defect datasets, rather than on proprietary ones.

Software development organizations often need to balance productivity and sustainability. To keep this balance, attention needs to be given as to why defects are re-assigned. This paper presents an empirical study to explore the causal relationships, other than probabilistic dependencies, between re-assigned/fixed defects and defect attributes: (i) Priority, (ii) Severity, (iii) Density, and (iv) Source. We pursue a quantitative approach based on Bayesian belief networks to uncover the inferential information encoded by these networks. The data source of this research comes from an issue tracking system repository of a proprietary and enterprise level software life-cycle management tool. The causal structure of the defect attributes in our domain has been estimated statistically and the results are plotted. The causes of a defect to be fixed and to be fixed after it has been re-assigned have been explored as well in this study. It has been observed that severity is an effect rather than a cause that significantly affect a defect to be re-assigned in our domain. However, the change of the bug severity has been observed to be a direct cause for a defect to be fixed. We believe that understanding the basis and causes of re-assigning and fixing bugs would help software development organizations to better allocate their resources in the software maintenance phase.

The Twitter Bullishness Index (TBI) has previously been reported to be a social media analytics indicator for the stock market. We explore the different components that shape the TBI. First, we determine the users to be dominated by the US and the English-speaking world. Second, we applied the Natural Language Processing tool term frequency--inverse document frequency (TFIDF) to explore the tweet content that contribute to the TBI. We revealed that the keyword lexicon associated with the terms bullish and bearish differs. Finally, we used the vector autoregression framework. Our results do not show the TBI to be a leading indicator for the returns of the Dow Jones Industrial Average (DJIA) within statistical significance. However, a Pearson correlation of 0.49 is observed between the TBI and the DJIA return.

The continuous growth of e-commerce has stimulated great interest in generating theories and models for online consumer behavior. While studies on online consumer behavior are widespread, research on relating Internet browsing activities to online shopping behavior are scarce. This paper provides an exploratory analysis on the relationship between online browsing habits and consumers' pre-shopping effort, as one of the indicators of shopping behavior. The data used in this study was extracted from 88,637 users with more than half a million shopping instances from two large online retailers, Amazon and Walmart. Our findings provide insights for scholars to form hypotheses and design models or theories to explain online consumer behavior. Practitioners may also use the results of this study to make strategic decisions.

In software engineering, maintainability is related to investigating the defects and their causes, correcting the defects and modifying the system to meet customer requirements. Maintenance is a time consuming activity within the software life cycle. Therefore, there is a need for efficiently organizing the software resources in terms of time, cost and personnel for maintenance activity. One way of efficiently managing maintenance resources is to predict defects that may occur after the deployment. Many researchers so far have built defect prediction models using different sets of metrics such as churn and static code metrics. However, hidden causes of defects such as code smells have not been investigated thoroughly. In this study we propose using data science and analytics techniques on software data to build defect prediction models. In order to build the prediction model we used code smells metrics, churn metrics and combination of churn and code smells metrics. The results of our experiments on two different software companies show that code smells is a good indicator of defect proneness of the software product. Therefore, we recommend that code smells metrics should be used to train a defect prediction model to guide the software maintenance team.

Defect prediction models presented in the literature lack generalization unless the original study can be replicated using new datasets and in different organizational settings. Practitioners can also benefit from replicating studies in their own environment by gaining insights and comparing their findings with those reported. In this work, we replicated an earlier study in order to investigate the merits of organizational metrics in building defect prediction models for large-scale enterprise software. We mined the organizational, code complexity, code churn and pre-release bug metrics of that large scale software and built defect prediction models for each metric set. In the original study, organizational metrics were found to achieve the highest performance. In our case, models based on organizational metrics performed better than models based on churn metrics but were outperformed by pre-release metric models. Further, we verified four individual organizational metrics as indicators for defects. We conclude that the performance of different metric sets in building defect prediction models depends on the project's characteristics and the targeted prediction level. Our replication of earlier research enabled assessing the validity and limitations of organizational metrics in a different context.

This workshop is the fourth in the series and continued to build upon the work carried out at the previous iterations of the International Workshop on Realizing Artificial Intelligence Synergies in Software Engineering, which were held at ICSE in 2012, 2013 and 2014. RAISE 2015 brought together researchers and practitioners from the artificial intelligence (AI) and software engineering (SE) disciplines to build on the interdis- ciplinary synergies that exist and to stimulate further interaction across these disciplines. Mutually beneficial characteristics have appeared in the past few decades and are still evolving due to new challenges and technological advances. Hence, the question that motivates and drives the RAISE Workshop series is: "Are SE and AI researchers ignoring important insights from AI and SE?". To pursue this question, RAISE'15 explored not only the application of AI techniques to SE problems but also the application of SE techniques to AI problems. RAISE not only strengthens the AI- and-SE community but also continues to develop a roadmap of strategic research directions for AI and SE.

The bug tracking repositories of software projects capture initial defect (bug) reports and the history of interactions among developers, testers, and customers. Extracting and mining information from these repositories is time consuming and daunting. Researchers have focused mostly on analyzing the frequency of the occurrence of defects and their attributes (e.g., The number of comments and lines of code changed, count of developers). However, the counting process eliminates information about the temporal alignment of events leading to changes in the attributes count. Software quality teams could plan and prioritize their work more efficiently if they were aware of these temporal sequences and knew their frequency of occurrence. In this paper, we introduce a novel dataset mined from the Fire fox bug repository (Bugzilla) which contains information about the temporal alignment of developer interactions. Our dataset covers eight years of data from the Fire fox project on activities throughout the project's lifecycle. Some of these activities have not been reported in frequency-based or other temporal datasets. The dataset we mined from the Fire fox project contains new activities, such as reporter experience, file exchange events, code-review process activities, and setting of milestones. We believe that this new dataset will improve analysis of bug reports and enable mining of temporal relationships so that practitioners can enhance their bug-fixing process.

Evolutionary coupling is defined as the implicit relationship between two or more software artifacts that are frequently changed together. In this study we investigate the effect of evolutionary coupling on defect proneness of a large financial legacy software in an industrial software development environment. We collected historical data for 5 years from 3 different software repositories containing 150 thousand files on 274 modules. Our results indicate that there is a positive correlation between evolutionary coupling and defect measures. Furthermore, we built linear and logistic regression models by using evolutionary coupling measures in order to explain defects. Although regression analysis results show that evolutionary coupling measures can be useful to explain defects, especially for modules in which high correlation is detected, explanatory power decreases dramatically with the decreasing correlation.

Context: Number of defects fixed in a given month is used as an input for several project management decisions such as release time, maintenance effort estimation and software quality assessment. Past activity of developers and testers may help us understand the future number of reported defects. Goal: To find a simple and easy to implement solution, predicting defect exposure. Method: We propose a temporal collaboration network model that uses the history of collaboration among developers, testers, and other issue originators to estimate the defect exposure for the next month. Results: Our empirical results show that temporal collaboration model could be used to predict the number of exposed defects in the next month with R2 values of 0.73. We also show that temporality gives a more realistic picture of collaboration network compared to a static one. Conclusions: We believe that our novel approach may be used to better plan for the upcoming releases, helping managers to make evidence based decisions.

Recommendation systems in software engineering (SE) should be designed to integrate evidence into practitioners experience. Bayesian networks (BNs) provide a natural statistical framework for evidence-based decision-making by incorporating an integrated summary of the available evidence and associated uncertainty (of consequences). In this study, we follow the lead of computational biology and healthcare decision-making, and investigate the applications of BNs in SE in terms of 1) main software engineering challenges addressed, 2) techniques used to learn causal relationships among variables, 3) techniques used to infer the parameters, and 4) variable types used as BN nodes. We conduct a systematic mapping study to investigate each of these four facets and compare the current usage of BNs in SE with these two domains. Subsequently, we highlight the main limitations of the usage of BNs in SE and propose a Hybrid BN to improve evidence-based decision-making in SE. In two industrial cases, we build sample hybrid BNs and evaluate their performance. The results of our empirical analyses show that hybrid BNs are powerful frameworks that combine expert knowledge with quantitative data. As researchers in SE become more aware of the underlying dynamics of BNs, the proposed models will also advance and naturally contribute to evidence based-decision-making.

The goal of software metrics is the identification and measurement of the essential parameters that affect software development. Metrics can be used to improve software quality and productivity. Existing metrics in the literature are mostly product or process related. However, thought processes of people have a significant impact on software quality as software is designed, implemented and tested by people. Therefore, in defining new metrics, we need to take into account human cognitive aspects. Our research aims to address this need through the proposal of a new metric scheme to quantify a specific human cognitive aspect, namely "confirmation bias". In our previous research, in order to quantify confirmation bias, we defined a methodology to measure confirmation biases of people. In this research, we propose a metric suite that would be used by practitioners during daily decision making. Our proposed metric set consists of six metrics with a theoretical basis in cognitive psychology and measurement theory. Empirical sample of these metrics are collected from two software companies that are specialized in two different domains in order to demonstrate their feasibility. We suggest ways in which practitioners may use these metrics to improve software development process.

PROMISE conference is an annual forum for researchers and practitioners to present, discuss and exchange ideas, results, expertise and experiences in construction and/or application of prediction models in software engineering. Such models could be targeted at: planning, design, implementation, testing, maintenance, quality assurance, evaluation, process improvement, management, decision making, and risk assessment in software and systems development. PROMISE is distinguished from similar forums with its public data repository and focus on methodological details, providing a unique interdisciplinary venue for software engineering and machine learning communities, and seeking for verifiable and repeatable prediction models that are useful in practice.

This workshop introduces software architecture concepts and their incorporation into computer science and software engineering curricula. Participants will learn techniques used in industry to specify quality attributes critical to architecture and use those attributes to drive the system structure using common architectural styles. Exercises will demonstrate these techniques and explore pedagogical uses of the techniques in CS and SE classes. Sample computer science curricula with courses that integrate workshop material will be presented. Presenters will lead a brainstorming session to help participants develop practical methods for using the material in their courses. Participants will become part of a community of educators sharing educational resources in software architecture.

We present an integrated measurement and defect prediction tool: Dione. Our tool enables organizations to measure, monitor, and control product quality through learning based defect prediction. Similar existing tools either provide data collection and analytics, or work just as a prediction engine. Therefore, companies need to deal with multiple tools with incompatible interfaces in order to deploy a complete measurement and prediction solution. Dione provides a fully integrated solution where data extraction, defect prediction and reporting steps fit seamlessly. In this paper, we present the major functionality and architectural elements of Dione followed by an overview of our demonstration.

Boehm and Basili 2001) described the start-of-the art in defect reduction.Since then, there has been considerable research into data mining of defect data; e.g.Menzies et al. (2007).The data mining work has become less about defect reduction, and more about how to organize a project's test resources in order to improve product quality by (say) defining a procedure such that the modules most likely to contain defects are inspected first (Menzies et al. 2010).After a decade of intensive work into data mining to make best use of testing resources, it is time to ask: what have we learned from all that research?Some of that research offers success stories with (e.g.)

Background: There are too many design options for software effort estimators. How can we best explore them all? Aim: We seek aspects on general principles of effort estimation that can guide the design of effort estimators. Method: We identified the essential assumption of analogy-based effort estimation, i.e., the immediate neighbors of a project offer stable conclusions about that project. We test that assumption by generating a binary tree of clusters of effort data and comparing the variance of supertrees versus smaller subtrees. Results: For 10 data sets (from Coc81, Nasa93, Desharnais, Albrecht, ISBSG, and data from Turkish companies), we found: 1) The estimation variance of cluster subtrees is usually larger than that of cluster supertrees; 2) if analogy is restricted to the cluster trees with lower variance, then effort estimates have a significantly lower error (measured using MRE, AR, and Pred(25) with a Wilcoxon test, 95 percent confidence, compared to nearest neighbor methods that use neighborhoods of a fixed size). Conclusion: Estimation by analogy can be significantly improved by a dynamic selection of nearest neighbors, using only the project data from regions with small variance.

This workshop introduces the concepts of software architecture and how to incorporate these concepts into the computer science and software engineering curriculum. Participants will learn techniques used in industry to specify quality attributes critical to system performance, modifiability, and availability, and to use those attributes to drive the system structure using well-known architectural styles. Exercises will be used to demonstrate the techniques and to practice effective methods for students to learn the techniques in CS and SE classes. Participants will become part of the community of educators sharing educational resources in software architecture.

In this paper, we consider a spectrum trading network. The primary service providers (PSPs), which are the long term owners of chunks of frequency spectrum, lease portions of their licensed frequency bands to secondary users for short term basis. The PSPs have also their own regular customers who expect to receive a minimum amount of bandwidth and a minimum level of quality of service. The existence of various network elements that want to maximize its own profits makes the problem very complex, with usually conflicting objective functions. The proposed framework in this paper aims at investigating the impact of power emission of secondary users to the pricing process. We have used a model based on game theory for PSPs' pricing problem to provide with a well defined equilibrium. The spectrum demand of secondary users is modeled using the multi-nomial logit (MNL) which is a kind of discrete choice model. We have used the MNL model to estimate the probability that a secondary user chooses a particular PSP as the seller. The application of the framework is shown on a simple demonstrative example. The results show that the proposed framework allows PSPs to make additional profit while preserving the social welfare of the network.

People are the most important pillar of software development process. It is critical to understand how they interact with each other and how these interactions affect the quality of the end product in terms of defects. In this research we propose to include a new set of metrics, a.k.a. social network metrics on issue repositories in predicting defects. Social network metrics on issue repositories has not been used before to predict defect proneness of a software product. To validate our hypotheses we used two datasets, development data of IBM1 Rational ® Team Concert™ (RTC) and Drupal, to conduct our experiments. The results of the experiments revealed that compared to other set of metrics such as churn metrics using social network metrics on issue repositories either considerably decreases high false alarm rates without compromising the detection rates or considerably increases low prediction rates without compromising low false alarm rates. Therefore we recommend practitioners to collect social network metrics on issue repositories since people related information is a strong indicator of past patterns in a given team.

In cognitive psychology, confirmation bias is defined as the tendency of people to verify hypotheses rather than refuting them. During unit testing software developers should aim to fail their code. However, due to confirmation bias, most defects might be overlooked leading to an increase in software defect density. In this research, we empirically analyze the effect of confirmation bias of software developers on software defect density.

The underutilization of spectrum coupled with developments in network technologies has prompted a number of proposals for managing spectrum. Dynamic spectrum access radio technology, which is based on cognitive radio technology, promises to increase spectrum sharing and thus overcome the lack of available spectrum for new communication services. In this paper, the pricing and the transmission power control processes are investigated in a cognitive radio network. The considered network consists of multiple primary service providers which have some unutilized bandwidth; and multiple secondary users that require spectrum bands. In this multiple-seller and multiple-buyer environment, the proposed framework aims at determining the optimum price values for unit spectrum bands that maximizes PSPs profits while protecting the social welfare of their network. Furthermore, the framework considers the power control, especially the effect of transmission power on the profit of PSPs. Modeling the competitive relationships among network elements as games ensures analyzing all elements' behaviors and actions in a formalized way. The existence of various network elements that want to maximize its own profits makes the problem very complex, with usually conflicting objective functions. We have used a model based on game theory for PSPs' pricing problem to provide with a well defined equilibrium. The simulation results show that the proposed framework allow PSPs to make up to 45%-86% of additional profit while preserving the social welfare of the network.

Software defect prediction aims to reduce software testing efforts by guiding testers through the defect-prone sections of software systems. Defect predictors are widely used in organizations to predict defects in order to save time and effort as an alternative to other techniques such as manual code reviews. The application of a defect prediction model in a real-life setting is difficult because it requires software metrics and defect data from past projects to predict the defect-proneness of new projects. It is, on the other hand, very practical because it is easy to apply, can detect defects using less time and reduces the testing effort. We have built a learning-based defect prediction model for a telecommunication company during a period of one year. In this study, we have briefly explained our model, presented its pay-off and described how we have implemented the model in the company. Furthermore, we have compared the performance of our model with that of another testing strategy applied in a pilot project that implemented a new process called Team Software Process (TSP). Our results show that defect predictors can be used as supportive tools during a new process implementation, predict 75% of code defects, and decrease the testing time compared with 25% of the code defects detected through more labor-intensive strategies such as code reviews and formal checklists.

In in-vitro fertilization (IVF) treatment, blastocyst stage embryo transfers at day 5 result in higher pregnancy rates. However, there is a risk of transfer cancelation due to embryonic developmental failure. Clinicians need reliable models in predicting blastocyst development. In this study, we apply Bayesian networks in order to investigate cause-effect relationships of the variables of interest in embryo growth process and to predict blastocyst development. We have analyzed 7745 embryo records including embryo morphological characteristics and patient related data. Experimental results revealed that, Bayesian networks can predict blastocyst development with 63.5% true positive rate and 33.8% false positive rate.

In this paper, we present a preliminary analysis of factors such as company culture, education and experience, on confirmation bias levels of software developers and testers. Confirmation bias is defined as the tendency of people to verify their hypotheses rather than refuting them and thus it has an effect on all software testing.

Software defect data has an imbalanced and highly skewed class distribution. The misclassification costs of two classes are not equal nor are known. It is critical to find the optimum bound, i.e. threshold, which would best separate defective and defect-free classes in software data. We have applied decision threshold optimization on Naïve Bayes classifier in order to find the optimum threshold for software defect data. ROC analyses show that decision threshold optimization significantly decreases false alarms (on the average by 11%) without changing probability of detection rates.

The traditional static spectrum access approach, which assigns a fixed portion of the spectrum to a specific license holder for exclusive use, is unable to manage the spectrum efficiently any longer. In an effort to improve the efficiency of its usage, alternative spectrum allocation scenarios are being proposed. One of these technologies is the Dynamic Spectrum Access which enables wireless users to share a wide range of available spectrum in an opportunistic manner. In this paper, we study an architecture for a competitive spectrum exchange marketplace, a theoretic base, and the empirical work for spectrum price formation. The competitive spectrum exchange marketplace architecture considers short term sub-lease of unutilized spectrum bands to different service providers. Our proposed pricing model applies game theory as its mathematical base. The Nash equilibrium point tells the spectrum holders the ideal price values where profit is maximized at the highest level of customer satisfaction. Our empirical results prove that the service providers' demand depends on the price and QoS of that band as well as the price and QoS offering of its competitors.

In the last decades, the development of the mobile telecommunication industry has triggered the increase of demand for wireless spectrum. Dynamic spectrum management (DSM) concept has been emerged with the development of cognitive radio technologies just to enable efficient utilization of the scarce spectrum. However, DSM will provide significant economic and social benefits only if it becomes widely utilized. For this to occur, the next generation wireless market itself must evolve. This paper analyzes a novel brokering architecture for next generation cognitive radio-based communication platform. We develop a simple competitive economic model among the players of our proposed architecture.

In this paper, we present a defect prediction model based on ensemble of classifiers, which has not been fully explored so far in this type of research. We have conducted several experiments on public datasets. Our results reveal that ensemble of classifiers considerably improve the defect detection capability compared to Naive Bayes algorithm. We also conduct a cost-benefit analysis for our ensemble, where it turns out that it is enough to inspect 32% of the code on the average, for detecting 76% of the defects.

Companies usually have limited amount of data for effort estimation. Machine learning methods have been preferred over parametric models due to their flexibility to calibrate the model for the available data. On the other hand, as machine learning methods become more complex they need more data to learn from. Therefore the challenge is to increase the performance of the algorithm when there is limited data. In this research we used a relatively complex machine learning algorithm, neural networks, and showed that stable and accurate estimations are achievable with an ensemble using associative memory. Our experimental results revealed that our proposed algorithm (ENNA) achieves on the average PRED(25) = 36.4 which is a significant increase compared to Neural Network (NN) PRED(25) = 8.

This research investigates the effects of linear and non-linear feature extraction methods on the cost estimation performance. We use principal component analysis (PCA) and Isomap for extracting new features from observed ones and evaluate these methods with support vector regression (SVR) on publicly available datasets. Our results for these datasets indicate there is no significant difference between the performances of these linear and non-linear feature extraction methods.

Web service discovery is currently performed with centralized registries such as UDDI. In this paper, we propose a super-peer network protocol to combine the efficiency of a centralized protocols and P2P networks. For avoiding a flooding the network with search request and for minimizing the number of messages routed in the network, we represented content-addressable network (CAN) structure, which provides a scalable, fault-tolerant distributed hash table (DHT), for super-peers communication. Web service definitions implemented semantically as OWL ontology. The proposed architecture offers self-maintaining and self-clustering network where the peer groups classify the Web service definitions and each peer-group becomes the owner of a classification dynamically.

Assigning projects of group work in the context of software engineering courses has become a commonly used practice in several educational institutions. Previously reported results examined different aspects of this approach. The problem is that most studies are based on relatively small group sizes. In this article a large scale project template for a class-wide project that is currently in use in the Department of Computer Engineering, Bogazici University, will be presented.

The approach presented in this paper aims at establishing a bandwidth allocation scheme for use in Geostationary Earth Orbit (GEO) satellite networks. As various multimedia applications necessitate large amounts of capacity, an efficient bandwidth management algorithm comes into prominence in order to provide high quality of service (QoS) to users. The first stage of the algorithm involves determining the portion of bandwidth that should be offered to a Satellite Gateway (SG), while the second stage consists of sharing this allocated bandwidth among different traffic flow types. At this point, the strategic pricing concept which means building a balance between the customer's desires and the network operator's profits emerges. The proposed approach has the objective to solve simultaneously these two consecutive allocation problems and moreover to propose their pricing scheme. Analytical and simulation results demonstrate the effectiveness of the suggested approach in terms of efficient bandwidth usage.

Pricing the future services in next generation mobile networks will play a key role from the operator point of view to achieve the maximum revenue and utilization rate. Moreover, pricing these services is a very important issue to users since the acceptance of the services is directly related to the quality of service (QoS) offered to their subscribers. Network operators who provide different wireless services face two major challenges: (1) social welfare maximization; and (2) price determination. In this article, we focus on proposing a simple and robust charging approach for wireless applications. We aim to publish a unit price for each wireless product class taking into account the issues of social welfare maximization and price determination. When compared with the flat-rate model, where the fee is fixed and does not vary with the actual usage of the bandwidth, the suggested solution is able to provide a better quality of service to the users as well as greater revenue to the network operator.