Sistem Multi-Agent Cerdas Penguji Perangkat Lunak Secara Otomatis

Sistem pengujian perangkat lunak otomatis menggunakan metode hybrid testing yang mengkombinasikan metode unit testing, functional testing, dan white box testing. Sistem pengujian akan dijadikan basis pengetahuan dan kecerdasan dari sistem multiagent. Performa sistem yang dihasilkan akan diuji dan dianalisis untuk dijadikan acuan merancang sistem auto-debugging untuk mempermudah dan mempercepat tahapan pengujian perangkat lunak. Sistem multi-agent cerdas yang dikembangkan akan terdiri dari 4 macam agen, yaitu main agent, agen penguji unit testing, agen penguji functional testing, agen penguji white box testing. Keempat agen akan memiliki basis pengetahuan yang berbeda-beda sesuai dengan tugas masing-masing. Namun demikian, semua agen akan memiliki kesamaan dalam hal kemampuan berkomunikasi, autonomy, dan berkolaborasi guna mencapai tujuan sistem. Luaran yang dihasilkan meliputi hasil evaluasi dan pengujian terhadap performa sistem berbasis multi-agent yang telah dikembangkan; hasil analisis terhadap pengujian dari performa sistem berbasis multi-agent; serta rancangan sistem auto-debuging untuk melakukan perbaikan secara otomatis terhadap debug yang ditemukan sistem penguji. Dengan demikian kontribusi dari penelitian ini terhadap bidang rekayasa perangkat lunak khususnya software testing dapat semakin optima

Cloud engineering is search based software engineering too

Many of the problems posed by the migration of computation to cloud platforms can be formulated and solved using techniques associated with Search Based Software Engineering (SBSE). Much of cloud software engineering involves problems of optimisation: performance, allocation, assignment and the dynamic balancing of resources to achieve pragmatic trade-offs between many competing technical and business objectives. SBSE is concerned with the application of computational search and optimisation to solve precisely these kinds of software engineering challenges. Interest in both cloud computing and SBSE has grown rapidly in the past five years, yet there has been little work on SBSE as a means of addressing cloud computing challenges. Like many computationally demanding activities, SBSE has the potential to benefit from the cloud; ‘SBSE in the cloud’. However, this paper focuses, instead, of the ways in which SBSE can benefit cloud computing. It thus develops the theme of ‘SBSE for the cloud’, formulating cloud computing challenges in ways that can be addressed using SBSE

Sapienz: Multi-objective automated testing for android applications

We introduce Sapienz, an approach to Android testing that uses multi-objective search-based testing to automatically explore and optimise test sequences, minimising length, while simultaneously maximising coverage and fault revelation. Sapienz combines random fuzzing, systematic and search-based exploration, exploiting seeding and multi-level instrumentation. Sapienz significantly outperforms (with large effect size) both the state-of-the-art technique Dynodroid and the widely-used tool, Android Monkey, in 7/10 experiments for coverage, 7/10 for fault detection and 10/10 for fault-revealing sequence length. When applied to the top 1, 000 Google Play apps, Sapienz found 558 unique, previously unknown crashes. So far we have managed to make contact with the developers of 27 crashing apps. Of these, 14 have confirmed that the crashes are caused by real faults. Of those 14, six already have developer-confirmed fixes

APLIKASI DETEKSI DINI UNTUK MENGENALI ANAK BERKEBUTUHAN KHUSUS MENGGUNAKAN METODE BUSINESS INTELLIGENCE

Anak berkebutuhan khusus dapat ditemui pada beberapa sekolah, baik sekolah reguler maupun non reguler. Terkadang keberadaan anak berkebutuhan khusus disekolah tidak disadari oleh guru, karena kurangnya kompetensi guru untuk mengenali anak berkebutuhan khusus. Apabila hal ini dibiarkan, maka akan sulit untuk menangani anak berkebutuhan khusus, karena kebiasaan anak sudah sulit untuk diubah. Melalui penelitian ini menerapkan sebuah pendekatan baru menggunakan metode business intelligence dengan model Klasifikasi: algoritma C4.5 dan Naïve Bayes, metode ini digunakan untuk membantu proses deteksi dini untuk mengenali anak berkebutuhan khusus. Algoritma C4.5 digunakan untuk menciptakan pola, sehingga didapatkan atribut yang paling berpengaruh sampai yang tidak terlalu berpengaruh dari dataset. Nilai AUC(Area Under Curve) dan Akurasi sebagai model evaluasi. Dan Model perbandingan yang digunakan yaitu Metode Parametrik, Paired T-Test. Jenis berkebutuhan khusus yang digunakan sebagai kategori adalah Attention Deficit Hyperactive Disorder(ADHD), Autism Spectrum Disorder(ASD), Slow Learner, Tuna Laras.  Aplikasi web dibangun sebagai sarana untuk melakukan proses deteksi dini. Hasil dari penelitian ini akan memberikan kategori bagi setiap anak, baik berkebutuhan khusus maupun normal. Penelitian ini dilakukan pada TK Kristen Kalam Kudus III Kosambi Baru Jakarta. Kata kunci: Anak berkebutuhan khusus, Metode Business Intelligence, Model Klasifikasi, Algoritma C4.5, Naïve Baye

Optimised Realistic Test Input Generation Using Web Services

Abstract. We introduce a multi-objective formulation of service-oriented testing, focusing on the balance between service price and reliability. We experimented with NSGA2 for this problem, investigating the effect on performance and quality of composition size, topology and the number of services discovered. For topologies small enough for exhaustive search we found that NSGA2 finds a pareto front very near (the fronts are a Euclidean distance of ∼ 0.00024 price-reliability points apart) the true pareto front. Regarding performance, we find that composition size has the strongest effect, with smaller topologies consuming more machine time; a curious effect we believe is due to the influence of crowding dis-tance. Regarding result quality, our results reveal that size and topology have more effect on the front found than the number of service choices discovered. As expected the cost-reliability relationship (logarithmic, lin-ear, exponential) is replicated in the front discovered when correlation is high, but as the price-reliability correlation decreases, we find fewer solutions on the front and the front becomes less smooth.

Search-based crash reproduction using behavioural model seeding

Search-based crash reproduction approaches assist developers during debugging by generating a test case which reproduces a crash given its stack trace. One of the fundamental steps of this approach is creating objects needed to trigger the crash. One way to overcome this limitation is seeding: using information about the application during the search process. With seeding, the existing usages of classes can be used in the search process to produce realistic sequences of method calls which create the required objects. In this study, we introduce behavioral model seeding: a new seeding method which learns class usages from both the system under test and existing test cases. Learned usages are then synthesized in a behavioral model (state machine). Then, this model serves to guide the evolutionary process. To assess behavioral model-seeding, we evaluate it against test-seeding (the state-of-the-art technique for seeding realistic objects) and no-seeding (without seeding any class usage). For this evaluation, we use a benchmark of 124 hard-to-reproduce crashes stemming from six open-source projects. Our results indicate that behavioral model-seeding outperforms both test seeding and no-seeding by a minimum of 6% without any notable negative impact on efficiency