Hybrid Algorithms for the Search of Test Data in SPL a�� Javier Ferrer

In Software Product Lines (SPLs) it is not possible, in general, to test all products of the family. The number of products denoted by a SPL is very high due to the combinatorial explosion of features. For this reason, some coverage criteria have been proposed which try to test at least all feature interactions without the necessity to test all products, e.g., all pairs of features (pairwise coverage). In addition, it is desirable to first test products composed by a set of priority features. This problem is known as the Prioritized Pairwise Test Data Generation Problem.

In our work we propose hybrid algorithms to generate prioritized test suites. The first one is based on an integer linear formulation and the second one is based on a integer quadratic (nonlinear) formulation. We compare these techniques with two state-of-the-art algorithms, the Parallel Prioritized Genetic Solver (PPGS) and a greedy algorithm called prioritized-ICPL. Our study reveals that our hybrid nonlinear approach is clearly the best in both, solution quality and computation time.

Presentation:A�ferrer-SS-SBSE2017

Deep Parameter Optimisation on Android Smartphones for Energy Minimisation a�� Markus Wagner

With power demands of mobile devices rising, it is becoming increasinglyA�important to make mobile software applications more energy efficient.A�Unfortunately, mobile platforms are diverse and very complex which makesA�energy behaviours difficult to model. This complexity presents challenges to theA�effectiveness of off-line optimisation of mobile applications. In this paper, weA�demonstrate that it is possible to automatically optimise an application for energyA�on a mobile device by evaluating energy consumption “in-vivo”. In contrast toA�previous work, we use only the device’s own internal meter. Our approachA�involves many technical challenges but represents a realistic path toward learningA�hardware specific energy models for program code features.

Presentation:A�sbse-deepparameter

Automated Software Development Support through Optimized Code History Models a�� Francisco Servant

Software developers regularly need to find diverse information to successfullyA�perform their tasks. Some example of software information needs are: “why wasA�this code implemented in this way?”, or “who has expertise in this functionality?”A�Unfortunately, finding such information requires high effort, and it is often foundA�inaccurately, which not only decreases software development productivity, but itA�also decreases software quality. In my research, I follow the insight that many ofA�the questions that developers ask can be answered automatically by analyzing theA�data that they produced in past software development tasks. I will present a seriesA�of techniques that automate the multi-revision, fine-grained analysis of source code history. These techniques provide high accuracy by optimizing codeA�similarity over its modeled history. I will also demonstrate how these techniquesA�help software developers to find relevant information about software developmentA�tasks efficiently and accurately.

Maintenance of the logical consistency in Cassandra – Pablo SuA?rez-A�tero

Contrary to the relational databases, in NoSQL databases like Cassandra is veryA�common that duplicity of data in different tables happens. This is because usuallyA�tables are query driven (designed based on queries) and there are not anyA�relationships between them, in order to increase the performance of the queries.A�Therefore, if the data is not updated on a proper way, inconsistencies in the storedA�information could appear. It is quite easy to introduce defaults that causeA�inconsistencies of the data in Cassandra, especially during the evolution of aA�system where new tables are created, being these ones hard to detect usingA�conventional techniques of dynamic testing. The developer is the one responsibleA�for the maintenance of this consistency incorporating and updating the properA�procedures. In this session, a preventive approach to these problems isA�introduced, establishing the procedures required to ensure the quality of data fromA�the point of view of their consistency and thus helping the developer. TheseA�procedures include a static analysis using the conceptual model, the queries andA�the logical model of the application. They also include the determination andA�execution of the operations that guarantee the consistency of the information.

Discovery of design patterns based on good practices -Rafael Barbudo

The complexity of current software systems obliges software engineers to learnA�about the good practices employed in previous projects. The use of design patterns is not an exception, as they can provide developers with a tool to improveA�the reusability and modularisation of their code. In this context, this talk willA�introduce a three-step prototypical model aimed at supporting software engineersA�to implement design patterns based on previous examples and successfulA�experiences. This model makes use of machine learning techniques like frequentA�pattern mining. A suitable representation of this knowledge will allow us to identifyA�potential code chunks which might become a design pattern.

Presentation:A�2ss-sbse

Agile Effort Estimation – Natasha Nigar

Software projects that are over-budget, delivered late, and fall short of usersa��A�expectations have been a challenge in software engineering for decades. TheA�success or failure of a software project heavily depends on the accuracy of effortA�estimation. The software project cost is primarily estimated based on effort whichA�is defined as the time taken by the software development team members forA�individual tasks completion. Therefore, accurate effort estimation has gainedA�highest importance due to exponential growth of large scale software applications.
This research contributes by presenting a novel approach for effort estimation inA�a�?Agile Software developmenta�� (ASD). In ASD, changes in customer requirementsA�are proactively incorporated while delivering software projects within budget andA�time. We shall formulate effort estimation as the search-based problem and useA�computational intelligence techniques, such as evolutionary algorithms, to addressA�following limitations in the current research for agile effort estimation.

  1. Datasets used for effort estimation contain single company projects data. WeA�will use cross-company data to validate our model.
  2. Other than scrum and XP no other agile method was investigated. We will useA�KANBAN agile method in our research.
  3. We will be first to use line of code (LOC) as size metric.A�The benefit of this research is that it will reduce the risk of software project fallingA�behind schedules by providing realistic estimation figures

Talks

Participants attending the summer school will have the opportunity to give a host talk on their ongoing research project. They will receive feedback from senior researchers in SBSE during the summer school. Information about talks will be available after the registration deadline.

Talks I

Wednesday, June 28 (12:00 – 13:30)

  • Agile Effort Estimation (Natasha Nigar)
  • Discovery of design patterns based on good practices (Rafael Barbudo)
  • Maintenance of the logical consistency in Cassandra (Pablo SuA?rez-A�tero)
  • Hybrid Algorithms for the Search of Test Data in SPL (Javier Ferrer)

Talks II

Friday, June 30 (12:00 – 13:00)

  • Automated Software Development Support through Optimized Code History Models (Francisco Servant)
  • Deep Parameter Optimisation on Android Smartphones for Energy Minimisation (Markus Wagner)