Context.Fault localization (FL) is a software lifecycle activity and its automation is a challenge for researchers and practitioners.Method.The study focuses on evolutionary fault localization and introduces a novel Genetic Programming (GP) approach that evolves FL heuristics based on the diversity of the suspiciousness score of program statements -a score to grade how faulty a statement is.Experimental analysis.The approach was evaluated against baselines, which include the canonical GP, in benchmarks with real programs and real faults.Conclusion.The results showed the competitiveness of the approach through evaluation metrics commonly used in the research field.

The research area of Software Defect Prediction (SDP) is both extensive and popular, and is often treated as a classification problem. Improvements in classification, pre-processing and tuning techniques, (together with many factors which can influence model performance) have encouraged this trend. However, no matter the effort in these areas, it seems that there is a ceiling in the performance of the classification models used in SDP. In this paper, the issue of classifier performance is analysed from the perspective of data complexity. Specifically, data complexity metrics are calculated using the Unified Bug Dataset, a collection of well-known SDP datasets, and then checked for correlation with the defect prediction performance of machine learning classifiers (in particular, the classifiers C5.0, Naive Bayes, Artificial Neural Networks, Random Forests, and Support Vector Machines). In this work, different domains of competence and incompetence are identified for the classifiers. Similarities and differences between the classifiers and the performance metrics are found and the Unified Bug Dataset is analysed from the perspective of data complexity. We found that certain classifiers work best in certain situations and that all data complexity metrics can be problematic, although certain classifiers did excel in some situations.

Fault localisation is an expensive and time-consuming stage of software maintenance. Research is continuing to develop new techniques to automate the process of reducing the effort needed for fault localisation without losing quality. For instance, spectrum-based techniques use execution information from testing to formulate measures for ranking a list of suspicious code locations at which the program may be defective: the suspiciousness formulae mainly combine variables related to code coverage and test results (pass or fail). Moreover previous research has evaluated mutation analysis data (mutation spectra) instead of coverage traces, to yield promising results. This paper reports on a Genetic Programming (GP) solution for the fault localisation problem together with a set of experiments to evaluate the GP solution with respect to baselines and benchmarks. The innovative aspects are the joint investigation of: (i) specialisation of suspiciousness formulae for certain contexts; (ii) the application of mutation spectra to GP-evolved formulae, i.e. signals other than program coverage; (iii) a comparison of the effectiveness of coverage spectra and mutation spectra in the context of evolutionary approaches; and (iv) an analysis of the mutation spectra quality. The results show the competitiveness of GP-evolved mutation spectra heuristics over coverage traces as well as over a number of baselines, and suggest that the quality of mutation-related variables increases the effectiveness of fault localisation heuristics.

In this short paper, we compare well-known rule/tree classifiers in software defect prediction with the CTC decision tree classifier designed to deal with class imbalanced. It is well-known that most software defect prediction datasets are highly imbalance (non-defective instances outnumber defective ones). In this work, we focused only on tree/rule classifiers as these are capable of explaining the decision, i.e., describing the metrics and thresholds that make a module error prone. Furthermore, rules/decision trees provide the advantage that they are easily understood and applied by project managers and quality assurance personnel. The CTC algorithm was designed to cope with class imbalance and noisy datasets instead of using preprocessing techniques (oversampling or undersampling), ensembles or cost weights of misclassification. The experimental work was carried out using the NASA datasets and results showed that induced CTC decision trees performed better or similar to the rest of the rule/tree classifiers.

Semi-Supervised Learning (SSL) is a data mining technique which comes between supervised and unsupervised techniques, and is useful when a small number of instances in a dataset are labelled but a lot of unlabelled data is also available. This is the case with user reviews in application stores such as the Apple App Store or Google Play, where a vast amount of reviews are available but classifying them into categories such as bug related review or feature request is expensive or at least labor intensive. SSL techniques are well-suited to this problem as classifying reviews not only takes time and effort, but may also be unnecessary. In this work, we analyse SSL techniques to show their viability and their capabilities in a dataset of reviews collected from the App Store for both transductive (predicting existing instance labels during training) and inductive (predicting labels on unseen future data) performance.

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.

Imbalanced data is a common problem in data mining when dealing with classification problems, where samples of a class vastly outnumber other classes. In this situation, many data mining algorithms generate poor models as they try to optimize the overall accuracy and perform badly in classes with very few samples. Software Engineering data in general and defect prediction datasets are not an exception and in this paper, we compare different approaches, namely sampling, cost-sensitive, ensemble and hybrid approaches to the problem of defect prediction with different datasets preprocessed differently. We have used the well-known NASA datasets curated by Shepperd et al. There are differences in the results depending on the characteristics of the dataset and the evaluation metrics, especially if duplicates and inconsistencies are removed as a preprocessing step.

The validation of software metrics has received much interest over the years due to a desire to promote metrics which are both well-founded theoretically and have also been shown empirically to reflect our intuition and be practically useful. In this paper we describe how we used automatic refactoring to investigate the changes which occur to a number of metrics as software evolves. We use the concept of software volatility to quantify our comparison of the metrics.

Effective management of diabetes is crucial for patient wellbeing and the prevention of low blood sugar levels (Hypoglycemia) and high blood sugar levels (Hyperglycemia) both of which can be potentially dangerous. Traditionally log books are maintained by patients to record information such as insulin usage and their meals. The ever increasing popularity of smart phones has resulted in various applications being developed to allow patients to log data and help manage their condition. However these applications are often developed simply for the logging of data and only occasionally provide basic calculations to suggest insulin doses following a meal. The goal of this research is to use case-based reasoning techniques to suggest an insulin dosage for the patient as opposed to using a one calculation fits all approach. This is to be achieved by building a knowledge base of the patient's history that is then used to obtain a solution which best fits the current circumstances. The proposed case-based reasoning system is described alongside the development of the system to date and discussion into further research and development. The final implementation will be tested and validated using a diabetic patient simulator to create a knowledge base and observe system behavior and accuracy.

The RAISE'13 workshop brought together researchers from the AI and software engineering disciplines to build on the interdisciplinary synergies which exist and to stimulate research across these disciplines. The first part of the workshop was devoted to current results and consisted of presentations and discussion of the state of the art. This was followed by a second part which looked over the horizon to seek future directions, inspired by a number of selected vision statements concerning the AI-and-SE crossover. The goal of the RAISE workshop was to strengthen the AI-and-SE community and also develop a roadmap of strategic research directions for AI and software engineering.

Requirements engineers need to be confident that enough requirements analysis has been done before a project can move forward. In the context of KAOS, this information can be derived from the soundness of the refinements: sound refinements indicate that the requirements in the goal-graph are mature enough or good enough for implementation. We can estimate how close we are to `good enough' requirements using the judgments of experts and other data from the goals. We apply Toulmin's model of argumentation to evaluate how sound refinements are. We then implement the resulting argumentation model using Bayesian Belief Networks and provide a semi-automated way aided by Natural Language Processing techniques to carry out the proposed evaluation. We have performed an initial validation on our work using a small case-study involving an electronic document management system.

Mobile app reviews are valuable repositories of ideas coming directly from app users. Such ideas span various topics, and in this paper we show that 23.3% of them represent feature requests, i.e. comments through which users either suggest new features for an app or express preferences for the re-design of already existing features of an app. One of the challenges app developers face when trying to make use of such feedback is the massive amount of available reviews. This makes it difficult to identify specific topics and recurring trends across reviews. Through this work, we aim to support such processes by designing MARA (Mobile App Review Analyzer), a prototype for automatic retrieval of mobile app feature requests from online reviews. The design of the prototype is a) informed by an investigation of the ways users express feature requests through reviews, b) developed around a set of pre-defined linguistic rules, and c) evaluated on a large sample of online reviews. The results of the evaluation were further analyzed using Latent Dirichlet Allocation for identifying common topics across feature requests, and the results of this analysis are reported in this paper.

This paper proposes an approach for modeling Social Media Collaborative Work (SMCW). We consider Social Media Collaborative Work to consist of multi-stakeholder viewpoints and human activity linked together by social media. SMCW has great potential within complex multifaceted domains such as healthcare. In this paper we describe how to model SMCW in a way which shows the multi-stakeholder intentions, concerns and priorities. We are conducting empirical studies to develop our approach for modeling SMCW. In particular we are using action research with a self-help community to develop and validate our SMCW modeling approach. In our approach we make use of the soft systems methodology in combination with i* modeling and social psychology.

Traditionally, simulation has been used by project managers in optimising decision making. However, current simulation packages only include simulation optimisation which considers a single objective (or multiple objectives combined into a single fitness function). This paper aims to describe an approach that consists of using multiobjective optimisation techniques via simulation in order to help software project managers find the best values for initial team size and schedule estimates for a given project so that cost, time and productivity are optimised. Using a System Dynamics (SD) simulation model of a software project, the sensitivity of the output variables regarding productivity, cost and schedule using different initial team size and schedule estimations is determined. The generated data is combined with a well-known multiobjective optimisation algorithm, NSGA-II, to find optimal solutions for the output variables. The NSGA-II algorithm was able to quickly converge to a set of optimal solutions composed of multiple and conflicting variables from a medium size software project simulation model. Multiobjective optimisation and SD simulation modeling are complementary techniques that can generate the Pareto front needed by project managers for decision making. Furthermore, visual representations of such solutions are intuitive and can help project managers in their decision making process.

This paper describes how the business case can be characterized and used to quickly make an initial and structurally complete goal-responsibility model. This eases the problem of bringing disciplined support to key decision makers in a development project in such a way that it can be instantiated quickly and thereafter support all key decision gateways. This process also greatly improves the understanding shared by the key decision makers and helps to identify and manage load-bearing assumptions.

Goal orientation is acknowledged as an important paradigm in requirements engineering. The structure of a goal-responsibility model provides opportunities for appraising the intention of a development. Creating a suitable model under agile constraints (time, incompleteness and catching up after an initial burst of creativity) can be challenging. Here we propose a marriage of UML activity diagrams with goal sketching in order to facilitate the production of goal-responsibility models under these constraints.

This paper describes a technique that can be used as part of a simple and practical agile method for requirements engineering. The technique can be used together with Agile Programming to develop software in internet time. We illustrate the technique and introduce lazy refinement, responsibility composition and context sketching. Goal sketching has been used in a number of real-world development projects, one of which is described here.

Aspect-orientation is an emerging paradigm that is based on the separation of concerns principle. It offers the idea of a new modular unit that encapsulates crosscutting concerns which would otherwise be scattered across multiple modules. Aspect-oriented measurement is a research area that has gained an increasing amount of attention lately due to the definition of several suites of measures designed to support aspect-oriented key features. Unlike object-orientation, however, aspect-orientation lacks a sound foundation on which measures can be expressed in operationalisable ways making it difficult to analyse and compare existing aspect-oriented measures. We identify a need to create common ground on which the definition of aspect-oriented measures can be based.

Abstract The SPE taxonomy of evolving software systems, first proposed by Lehman in 1980, is re‐examined in this work. The primary concepts of software evolution are related to generic theories of evolution, particularly Dawkins' concept of a replicator, to the hermeneutic tradition in philosophy and to Kuhn's concept of paradigm. These concepts provide the foundations that are needed for understanding the phenomenon of software evolution and for refining the definitions of the SPE categories. In particular, this work argues that a software system should be defined as of type P if its controlling stakeholders have made a strategic decision that the system must comply with a single paradigm in its representation of domain knowledge. The proposed refinement of SPE is expected to provide a more productive basis for developing testable hypotheses and models about possible differences in the evolution of E‐ and P‐type systems than is provided by the original scheme. Copyright © 2005 John Wiley & Sons, Ltd.

Consider the statement "this project should cost X and has risk of Y". Such statements are used daily in industry as the basis for making decisions. The work reported is part of a study aimed at providing a rational and pragmatic basis for such statements. Of particular interest are predictions made in the requirements and early phases of projects. A preliminary model has been constructed using Bayesian belief networks and in support of this, a programme to collect and study data during the execution of various software development projects commenced in May 2002. The data collection programme is undertaken under the constraints of a commercial industrial regime of multiple concurrent small to medium scale software development projects. Guided by pragmatism, the work is predicated on the use of data that can be collected readily by project managers; including expert judgements, effort, elapsed times and metrics collected within each project.

It has been claimed that dynamic prediction models can be used to help project managers make more accurate estimates than static prediction models. However, such a claim needs to be validated so that project managers can use dynamic models with confidence. In this paper we discuss an experiment we conducted in an academic environment that compared a dynamic model using Bayesian belief networks (BBN) with a static model involving the COCOMO and Akiyama models. The results from this experiment in fact validate the above claim. However we suggest replication of this experiment in order to increase confidence to our results.

The motivation behind the idea of product focused process improvement is to make a process improvement program address certain product quality features in an explicit manner. The PROFES methodology (http://www.profes.org) describes such an improvement program through the notion of a PPD (Product-Process Dependency) repository. The typed generic process model (TGPM) (Satpathy et al., 2000) is a parametric template which relates each process attribute with the way it is likely to affect the quality of the intermediate as well as the end products. TGPM could be instantiated to generate process models for individual processes. This paper shows how the TGPM and the PROFES methodology both address product quality in a similar manner and how they could be complementary to each other as regards to product focused process improvement.

In addition to managing day-to-day maintenance, information system managers need to be able to predict and plan the longer-term evolution of software systems on an objective, quantified basis. Currently this is a difficult task, since the dynamics of software evolution, and the characteristics of evolvable software are not clearly understood. In this paper we present an approach to understanding software evolution. The approach looks at software evolution from two different points of view. The dynamic viewpoint investigates how to model software evolution trends and the static viewpoint studies the characteristics of software artefacts to see what makes software systems more evolvable. The former will help engineers to foresee the actions to be taken in the evolution process, while the latter provides an objective, quantified basis to evaluate the software with respect to its ability to evolve and will help to produce more evolvable software systems.

The cost-effective and reliable evolution of systems is a significant software engineering challenge. Our approach is based on a combination of product modelling, process modelling and software metrics. We describe an empirical laboratory study following the evolution of three releases of a publicly available exemplar system. Analysis of the metrics which were collected improves our understanding of how a system evolves. Process metrics can provide information about how the product is put together, and product metrics suggest explanations for the development processes observed.

No abstract available.

Abstract This survey presents information concerning the use of functional languages (both strict and non-strict) for teaching in higher education. It lists the languages used by over 70 different institutions, the years in which the courses are given, and the recommended textbooks.